{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Idea here is to make a 2 param NN (x,y,loss), then do a grid search where we init model, do one epoch, record results. Then contour it.\n",
    "\n",
    "So first I need something to convert it to features.... then I have a linear(3) convert 3 features to one thing..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T07:01:40.941724Z",
     "start_time": "2017-11-17T07:01:40.930835Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['seed']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "%pylab inline\n",
    "import pickle\n",
    "import itertools\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T07:01:41.310288Z",
     "start_time": "2017-11-17T07:01:41.304198Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.colors import LogNorm\n",
    "from matplotlib import animation\n",
    "from IPython.display import HTML\n",
    "\n",
    "from collections import defaultdict\n",
    "from itertools import zip_longest\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T07:01:41.574944Z",
     "start_time": "2017-11-17T07:01:41.562939Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cuda=False\n",
    "seed=0\n",
    "batch_size=512\n",
    "\n",
    "\n",
    "ts = datetime.datetime.utcnow().strftime('%Y%m%d_%H-%M-%S')\n",
    "\n",
    "model_path = 'data/model_%s.pickle' % ts\n",
    "points_file = 'data/points_%s.pickle' % ts\n",
    "\n",
    "\n",
    "torch.manual_seed(seed)\n",
    "if cuda:\n",
    "    torch.cuda.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T06:26:43.175403Z",
     "start_time": "2017-11-17T06:26:43.156580Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=True, download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# test_loader = torch.utils.data.DataLoader(\n",
    "#     datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
    "#                        transforms.ToTensor(),\n",
    "#                        transforms.Normalize((0.1307,), (0.3081,))\n",
    "#                    ])),\n",
    "#     batch_size=args.test_batch_size, shuffle=True, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:38:14.953533Z",
     "start_time": "2017-11-18T00:38:14.931317Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# https://github.com/pytorch/examples/blob/master/mnist/main.py\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 2, kernel_size=28, stride=(28,28))\n",
    "#         self.conv2 = nn.Conv2d(10, 2, kernel_size=5)\n",
    "#         self.fc1 = nn.Linear(320, 2)\n",
    "        self.fc2 = nn.Linear(2, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.tanh(self.conv1(x))\n",
    "#         x = F.relu(F.max_pool2d(self.conv2(x), 2))\n",
    "        x = x.view(-1, 2)\n",
    "#         x = F.tanh(self.fc1(x))\n",
    "#         x = F.dropout(x, training=self.training)\n",
    "        x = self.fc2(x)\n",
    "        return F.sigmoid(x)\n",
    "\n",
    "model = Net()\n",
    "optimizer = optim.SGD(model.parameters(), lr=1e-3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T09:32:02.004112Z",
     "start_time": "2017-11-17T09:32:02.002021Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# nn.Conv2d?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pretrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T09:32:27.488827Z",
     "start_time": "2017-11-17T09:32:02.554839Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/torch/nn/functional.py:767: UserWarning: Using a target size (torch.Size([512])) that is different to the input size (torch.Size([512, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n",
      "/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/torch/nn/functional.py:767: UserWarning: Using a target size (torch.Size([96])) that is different to the input size (torch.Size([96, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [11232/60000 (99%)]\tLoss: 0.636986\n",
      "Train Epoch: 2 [11232/60000 (99%)]\tLoss: 0.622902\n",
      "Train Epoch: 3 [11232/60000 (99%)]\tLoss: 0.602751\n",
      "Train Epoch: 4 [11232/60000 (99%)]\tLoss: 0.579772\n",
      "Train Epoch: 5 [11232/60000 (99%)]\tLoss: 0.581861\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-205-68b33f9dff4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_workers\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# same-process loading\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m                 \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpin_memory_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_workers\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# same-process loading\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m                 \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpin_memory_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/torchvision/datasets/mnist.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m# doing this so that it is consistent with all other datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;31m# to return a PIL Image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'L'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mfromarray\u001b[0;34m(obj, mode)\u001b[0m\n\u001b[1;32m   2436\u001b[0m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtostring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2438\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfrombuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"raw\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrawmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mfrombuffer\u001b[0;34m(mode, size, data, decoder_name, *args)\u001b[0m\n\u001b[1;32m   2386\u001b[0m                 \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecoder_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2387\u001b[0m                 )\n\u001b[0;32m-> 2388\u001b[0;31m             \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadonly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2389\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# I got from 0.7 to 0.4 in 100 epochs\n",
    "epochs=200\n",
    "for epoch in range(1, epochs + 1):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        if cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        \n",
    "        # reduce this to a binary problem\n",
    "        target = (target>5).type(torch.FloatTensor)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.binary_cross_entropy(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "        epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "        100. * batch_idx / len(train_loader), loss.data[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:38:11.814672Z",
     "start_time": "2017-11-18T00:38:11.810165Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.22772104,  0.45285982]], dtype=float32), 5.7115297)"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minima = model.fc2.weight.data.numpy()\n",
    "minima_z = loss.data.numpy()[0]\n",
    "minima, minima_z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:38:21.392955Z",
     "start_time": "2017-11-18T00:38:21.385840Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/torch/serialization.py:147: UserWarning: Couldn't retrieve source code for container of type Net. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    }
   ],
   "source": [
    "\n",
    "torch.save(model, model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T05:23:35.326165Z",
     "start_time": "2017-11-17T05:23:35.320968Z"
    }
   },
   "source": [
    "# Explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:38:22.209251Z",
     "start_time": "2017-11-18T00:38:22.201845Z"
    }
   },
   "outputs": [],
   "source": [
    "model2 = torch.load(model_path)\n",
    "# freeze all except last layer\n",
    "mlayers = [\n",
    "    model2.conv1,\n",
    "#     model2.conv2,\n",
    "#     model2.fc1\n",
    "]\n",
    "for layer in mlayers:\n",
    "    for param in layer.parameters():\n",
    "          param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:38:22.728638Z",
     "start_time": "2017-11-18T00:38:22.725053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 2])"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fc2.weight.data.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T09:05:43.664127Z",
     "start_time": "2017-11-17T09:05:43.217513Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:38:23.540046Z",
     "start_time": "2017-11-18T00:38:23.537252Z"
    }
   },
   "outputs": [],
   "source": [
    "xm, ym = torch.Tensor(minima.T)\n",
    "xm, ym = xm.numpy()[0], ym.numpy()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:38:24.104919Z",
     "start_time": "2017-11-18T00:38:24.097195Z"
    }
   },
   "outputs": [],
   "source": [
    "xs=torch.arange(xm-40,xm+0,0.3)\n",
    "# xs = torch.Tensor(np.sort(np.random.normal(xm,100,100)))\n",
    "ys=torch.arange(ym-40,xm+40,0.3)\n",
    "# ys = torch.Tensor(np.sort(np.random.normal(ym,100,100)))\n",
    "xys = list(itertools.product(xs,ys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:38:35.349994Z",
     "start_time": "2017-11-18T00:38:35.345461Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xs=torch.arange(xm-20,xm+20,1)\n",
    "# xs = torch.Tensor(np.sort(np.random.normal(xm,100,100)))\n",
    "ys=torch.arange(ym-20,xm+20,1)\n",
    "# ys = torch.Tensor(np.sort(np.random.normal(ym,100,100)))\n",
    "xys = list(itertools.product(xs,ys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-11-18T00:38:35.576Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc3b53f51e4947c6a3cd6adbb0514b4c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/torch/nn/functional.py:767: UserWarning: Using a target size (torch.Size([512])) that is different to the input size (torch.Size([512, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# grid search, run this overnight\n",
    "points = []\n",
    "\n",
    "for x,y in tqdm(xys):\n",
    "    \n",
    "    torch.manual_seed(seed)\n",
    "    if cuda:\n",
    "        torch.cuda.manual_seed(seed)\n",
    "\n",
    "    # set last layer weights\n",
    "    model2.fc2.weight.data =  torch.Tensor([[y,x]])\n",
    "\n",
    "    zs=[]\n",
    "    dzs=[]\n",
    "    batches=0\n",
    "\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        if cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "\n",
    "        # reduce this to a binary problem\n",
    "        target = (target>5).type(torch.FloatTensor)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        output = model2(data)\n",
    "        loss = F.binary_cross_entropy(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        dzs.append(model2.fc2.weight.grad.data.numpy())\n",
    "        zs.append(loss.data.numpy()[0])\n",
    "        batches += 1 \n",
    "        \n",
    "        if batches>6:\n",
    "            break\n",
    "            \n",
    "    output = model2(data)\n",
    "    loss = F.binary_cross_entropy(output, target)\n",
    "\n",
    "    z = np.mean(zs)\n",
    "    z_var = np.std(zs)\n",
    "    dz = np.mean(dzs,0)\n",
    "    dz_var = np.std(dzs,0)\n",
    "    points.append([x,y,z, z_var, dz, dz_var, batches])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:37:19.723652Z",
     "start_time": "2017-11-18T00:37:19.716637Z"
    }
   },
   "outputs": [],
   "source": [
    "x = np.array([p[0] for p in points])\n",
    "y = np.array([p[1] for p in points])\n",
    "z = np.array([p[2] for p in points])\n",
    "zv = np.array([p[3] for p in points])\n",
    "dz = np.array([p[4] for p in points])[:,0,:]\n",
    "dzv = np.array([p[5] for p in points])[:,0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:37:20.466626Z",
     "start_time": "2017-11-18T00:37:20.206177Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NN/D390d6ejpmzpwpeHxjx441qOMxVGiCsiZNmsDOzg5hYWEvRUjff/+9XtZV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\ntZ/Cw8NJ7gW1q01JScEbb7yB0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/bsWaZOBzU5WGaccnfwV65cQX5+PtMkuaqqitlFOHPm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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ad9d08630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "# x,y,z,zv,dz,dzv, n = np.array(points).T\n",
    "plt.quiver(x, y, dz[:,0], dz[:,1], angles='xy', scale_units='xy', scale=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:37:21.178084Z",
     "start_time": "2017-11-18T00:37:20.909292Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SFRYWho6DgK6//nrdeOON+s1vfhM6CmLqjTfe0NVXX63LLrtMJSUloeNISpGSdd555ykr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\nAIAH/x+orPHXvXSsoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ad9a4fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "# x,y,z,zv,dz,dzv, n = np.array(points).T\n",
    "plt.quiver(x, y, dzv[:,0], dzv[:,1], angles='xy', scale_units='xy', scale=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:37:21.924363Z",
     "start_time": "2017-11-18T00:37:21.732391Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AlSwRERGRAKhkiYiIiARAJUtEREQkACpZIiIiIgFQyRIREREJgEqWiIiISABU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0adae59c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "# x,y,z,zv,dz,dzv, n = np.array(points).T\n",
    "plt.scatter(x,y,c=dz[:,0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:37:27.128137Z",
     "start_time": "2017-11-18T00:37:26.963641Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f0ad88e1898>"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J/3zvIY8jK03OOb55xyPEk2ky2VxinExnaI8luOWPyzyOzl9e2rC9W0ID0JlI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\nKaWUUllBKzVKKaWUygpaqVFKKaVUVvj/I9ztmxLHDy8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ad88e2908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(x,y,c=z)\n",
    "plt.title('loss')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:37:23.576169Z",
     "start_time": "2017-11-18T00:37:23.315761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f0adad59518>"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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joLC5e9Ax0NDQ4EuWLDmgx1i9aiuf+MfbSKcyZDI5IpEQ0WiEb950JbPnTBqk\nSKWrp1as45Pfu4d0Jks258QiYarjUX7+hXcxedzIoMOrGO7OZ27+HY8/v5bOZBqA6niUBcfO5l8/\neEHA0QnA/Y+t4N9+9BDJdBZ3Jx4NM2ZkDbd95d2MqqsOOjzpw4OPvci/3fpAl3aLMGpkNbf925WM\nGnHg7WZmS929YRBC3adRh070k29956A/7oMLvjNsr6E/yqb76b+++QCdHSkymRwAmUyOzs4U//3N\nBwKOrDy5O1++7SESqQzZXD4xTmWytHUkuem3jwUcXWX526pN3RIagM5kmkeWvcyLa7cGGJkApDNZ\n/v0nD5NIZdj9IzKZzrKjtYPb/3dZwNFJX9KZLN/40R/3aLcMza0d/OIPB/YjPAi7J98b7KXYlE1S\n88LyDX2uL4ZqVLnZ0dZBU2t7/p3SZcnlnL8+vzbg6CrLUy+sJZFK92iLdDbHky+sCzg6eWXD9kKb\neLcllc6waNmqoMOTPqzZ2EQu5z3fV+ksi5aUZrspqSkhVdWx3tdXxTArvn/4Ulcdi5LrI1ms7aMt\nZGiMrK0ihPX48A05jKzVmI2gjaypIpXO9GgfHEbWleDYjApRV1NFuo92G6V2K1plk9S86ZJjicW7\nj3uOxSJceMn8gCIqbzVVMWri0V63TRs3apijqWxHzJxELusYdFsymRxHHzI52OCEyfUjwXu2jwEH\nTxkXaGzSt0njR2BuvbbbjBJst92T76lSUyLed80CTjplFrFYhNq6OLFYhIaTDuH9Hzor6NDK0s72\nBB2JdK/bVm9uGuZoKtuylRsI93KWUyQc4ukV6wOISLp6dWMTkVDvH7UvvrJlmKOR/lq7cUefZw+u\nfEVj1YpV2ZzSHY2G+eJX3saWzS2sX9fE9IPG6XTuIeTQ5zROGsI0vBzAbPet14TM1BZFwJ0+u8DV\nPMWt73YrzZbzIqysDLayqdTsNmnyaE44aZYSmiE2qraKudPr2fM9H4uEueCkw4IJqkKdddzsXisB\nZrDguFkBRCRdHTJtHCN6GdtUFYtw8ZlHBRCR9MeMqWMZNaLn2JmqWISLFpRmu+WwQV+KTdklNTJ8\n/vUDFzCqtorqwtiamniUmZPGcPXFJwccWWWZOXksH7jkZOLRCJFwiEg4RDwa4SNvfQNTxmt8U9DM\njK9+/BJqqmNUxSIY+XmEjp47hTefdXTQ4UkfzIwbP1lot3ih3aqiHDFnMpeee0zQ4UkfymbyPQlG\nZzLNH5cCcY+TAAAgAElEQVS+xOamNg47aAKnHT2TcB/jB2Rord3SzKPLXsbMOPv4OUyboGplMWlr\nT/DHJ1ayo7WD+YdO5bgjpuvMzBKwqyPJH/+6kh0t7cw7bCrHHzl47Tack+/VzZ3k8296z6A/7mPn\nfqOoJt8rmzE1EozqeJSLTz0y6DAEmDFpDFddeGLQYUgfRtRWcdlC/cIvNXU1cS49Z17QYUg/KakR\nERGpAJUwUFhJjYiISNkrznllBpsGP4iIiEhZUKVGRESkAlRC95MqNSIiIlIWVKkREREpcw4aUyMi\nIiJSKlSpERERKXdeGdflU1IjIiJSAYrxWk2DTd1PIiIiUhZUqRERESlzjk7pFhERESkZqtSIiIiU\nPV0mAQAzm25mj5jZC2b2vJl9vLD+BjPbaGbPFpYLuxzzWTNbZWYrzey8oXwBIiIism/ug78Um/5U\najLAP7n7MjMbASw1s4cK277l7v/RdWczOwK4AjgSmAL80czmunt2MAMXERER6WqfSY27bwY2F263\nmdkKYOpeDnkzcIe7J4FXzWwVcCLw+CDEKyIiIgOggcJ7MLOZwLHAk4VV15nZc2b2IzMbU1g3FVjf\n5bAN9JIEmdk1ZrbEzJY0Njbud+AiIiIiXfU7qTGzOuA3wCfcfSdwMzALmE++kvOf+/PE7n6ruze4\ne0N9ff3+HCoiIiL7IT8GxgZ9KTb9OvvJzKLkE5qfu/tdAO6+tcv27wO/L9zdCEzvcvi0wjoREREJ\niM5+AszMgB8CK9z9m13WT+6y22XA8sLte4ErzCxuZgcDc4CnBi9kERERkZ76U6k5DbgS+LuZPVtY\n9zngnWY2n/xEhWuADwG4+/NmdifwAvkzpz6iM59ERESCVYynYA+2/pz9tBh6vQrWfXs55kbgxgOI\na0CymRxP/PVl1q3dzvSDxnHyaXOIRMLDHUZFSaYy/OWJl9myrZW5h0ykYf5MQqHyL3EWo01bWlj8\n5CoAzjhlDpMmjAo4IumqoyPJnxe/xI6Wdo4+YhpHHzmVfCFcill7Z4o/LX2Zpp3tHDN7CvPnqN2K\nWdnMKNzS3M7Hr/0Jzc3tJBNp4lVRRo+u4Tv/772MGVMbdHhlaePmZv7xM78gkUyTTGWIxyIcNG0s\n373xCqqrYkGHV1F++dun+f7P/vLaL7Fbf7qI695/FpdeeGywgQkAK1/ewqf+zy/J5nKk0xmi0Qjz\njprGV294K5GwrlZTrF5cu5Vrv/FrsrkcqXSGWDTC/NlT+NbHLi3JH8zFOLB3sJXNu+mm7zzI1i2t\ndHakyOWczo4U27bt5L+/+UDQoZWtr3zrD7Ts7KQzkc7/myfSvLp2O7f9UlMSDaf1m5r5/s8Wk0pl\nSafzSyqV5b9/9AhbG3cGHV7Fc3eu/9ff0t6RJJFIk806iUSav/19PX+4/29Bhyd9cHc+fdPv2NWZ\npDOZJptzOpNpnnl5I3ct+nvQ4e03Z/DPfCrGJKlskprFi1aSzea6rctmcvz1LyvxSuhIHGZtuxKs\nfGUrnvPC5V/zSyqV5YFHXwg6vIry57++lP+/7w7ZwuKOOyx6/OWgw6t4a9Y1sbOts8f88slkhvse\nKL0vx0qxdkszO3Z2gIPlwLJADhKpDPf8Zfk+j5dglE33E8pbhpW755PFXv7dc7lcz5UydNzxdBbL\ndGmMLOQsq4S+CLh77++JvtZLUci54zkIZV5fZ4DnSvczrhI+DcqmUnPy6XMI79E3HQ4bJ582R4O6\nhsDIEdXEwxEMeiyT6jVAdTjNnTURMt6jHTyV44i5k/d+sAy5mQeNI5Pu/QTQ6VPHDnM00l8zJ40h\nl8q3W9f3VchhZv2YvR0qASqbpOajnzyPceNHUF2TH6BaXRNj3PgRXPdJXSR8KLTtSpDq44O6sbFt\nmKOpbKtWbe31jLNw2Fi5cnMAEUlX69bvIBLp/aN246bmYY5G+mv91hYioVCvp/5u3NY67PEcMM0o\nXFrGjK3jx3d8mMcWrWTdmu1MnzGO0844lFisbF5iUcnlnL4KYDl1eQyrfFsYexaXQ6FQj3FmMvxy\nOSccCgE9fwTkcnqvFKtsNqd2K0Fl9Y0fjYZZsPCIoMOoCKNGVnPwjPGsWr2t24RO0UiYhWccFlxg\nFej0U+bw01/8tUcCYwannTInoKhkt5kzxlNTE6Mzke62Ph6PcN65RwUUlezLzCnjqKuJ05nco91i\nES48rUS/ZyogFyub7icZfl/41Juoq62iKp7PjaurokyZPJr3vvO0gCOrLDNnjOeKt59EPB4hFDLC\nYSMei/Ded5/O1Cnq+w9aKGTc8PlLqa6KEo+9/l6ZO2cSl1w4P+DopC+hkPHV6y6iOt6l3eJRDp0x\ngbcuPCbg6AamErqfrBjOjmhoaPAlS5YEHYYMQHtHkof/vIJNW1s5bM4kTj9pdklOSlUOVr/ayJ8X\nv4iZseANhzFzxvigQ5IuWlo6ePjRF9jetIv586ZzwvGHaPbtEtDS1skDj6+gsXkXxx02nZOPHrxZ\n081sqbs3DMqD7UPVrKk+/WsfHvTHXXX59cP2GvqjrLqfZPjV1sS55AL92iwGhxxczyEH1wcdhvRh\n9Oga3npp0Xz2Sz+NHlHNO954XNBhDIoiqGEMOXU/iYiISFlQpUZERKTMOZVx7SclNSIiIuXOgQpI\natT9JCIiImVBlRoREZEKoIHCIiIiIiVClRoREZFKEFClxszWAG3krzmR6W1eGzNbAHwbiALb3f3M\n/h7blZIaERGRshf4DMBnufv23jaY2WjgJuB8d19nZhP6e+ye1P0kIiIiQfoH4C53Xwfg7tsG+kBK\nakRERCqBD8HS/2d+0MyWmtk1vWyfC4wxs0cL+7xnP47tRt1PIiIiMlDjzazrxRtvdfdb99jndHff\nWOhWesjMXnT3RV22R4DjgYVANfC4mT3h7i/149hulNSIiIiUOx+yGYW372vwrrtvLPzdZmZ3AycC\nXROTDUCTu7cD7Wa2CDgGeKkfx3aj7icREREZEmZWa2Yjdt8G3ggs32O3e4DTzSxiZjXAScCKfh7b\njSo1IiIilSCYU7onAnebGeRzjl+4+/1mdi2Au9/i7ivM7H7gOSAH/MDdl5vZIb0du7cnU1IjIiJS\nEYb/lG53X02+K2nP9bfscf8bwDf6c+zeqPtJREREyoIqNSIiIpVA134SERERKQ2q1IiIiFSCCqjU\nKKmRA+LurFq5ma2bWph96GQmTR0TdEgVK5FI89zSNRhwTMPBxOJ6excTd+f5FzbS3NzBEYdPYdy4\nuqBDkn5wd5a/vJmm1naOnDWZ+rEl2m4OBHvtp2GhTz0ZsJ2tHXzuup+yfs12QqEQ6XSWNyw8gn++\n4VLCYfVsDqcnFq3k375wF6FQ/kPL3fnC195OwymzA45MALZu28k/ffoOdjS3YwaZTJbLLjmeD129\ngMLpqlKEtjbt5KM3/prtzbsImZHOZHnrufP56LvPVLsVKX3zyID9xw2/5dWXt5HoTNPRniSdyvDY\nIyv47e1PBB1aRWna3sZXP/drEp0pOtqTdLQn6exI8eVP38nOlo6gwxPgC//3N2ze0kJnZ4qOjhSp\nVJZ7fv8Mi/6yMujQZC8+8x/3sHFrC52JNO2dKVLpLHc//BwPP1Ga7eY++EuxUVIjA9LRnmTpE6+Q\nSWcg5/nFnWRnint/9XTQ4VWUPz/4PLndnzBd2gLgL396IeDoZOOmZtZv2EE2592uA9iZSHPXPUsD\njk76smlbK2s2FdrNeG3pTKa58/5ngg5P+qDuJxmQVDJd+ALtsrKQtXe0JwKJqVJ1tCdJJ9Ld2wJI\nJfKVGwlWR0cqf6OX3oq2Nr1XilV7ZxIMPLzHBoe2Uv2MK8LKymBTUiMDMmpMbZ9zU44dU6ID6UrU\n9JnjeiQ0AJ5xZh4yYfgDkm5mzhhHMpPpNamZMHHk8Ack/TJz6jgS2SzsOXbGYGJ9ibZbBQwUVveT\nDMiutgS5bO9pf2tz+zBHU9k2rdvR43MXwELG+jXbhz8g6Wb9xmYikT1/7udtb9o1zNFIf23Y2kK0\nj3ZrbNVnXLFSpUYGrK/B/6GQcuXhFDIjFA6RzXQv10QiaodiYEA4nD87cE8hnSVY1EJhg0zP9eES\n/YyzCuh+Ks2WkcCNGFnNrMMmY6HumU00FmHhm+YFFFVlOm3h4b2eQm9mnHbW4QFEJF3NnDGekSOq\neqyPxyO86byjA4hI+mPmlLGMGVHTY31VLMJFZxwZQETSH0pqZMA+c+NbGTmqhuqaGGZGdU2MGYfU\n8w9Xnxl0aBVl2ozxXPnhs4jFI0SiYSLRMLF4hA9+/FwmThkddHgVz8z48hcuo7YmRlVVlFDIqKqK\nMu+o6bzp/P26ALEMIzPj3z52MbXVMarjUUJmVMejzJszhbecXYI/3HyIliJjXgQnmjc0NPiSJUuC\nDkMGIJlIs/jhF9i6uYU5h03h+FNnqfspIBvXNfHXR1/EgNMXHqHZnYvMrvYkjy56keaWduYdNZ15\nR03TBG4lYFdnkj89+RJNrR0cM3cKxx42eO1mZkvdvWFQHmwf4jOm+eTPf3zQH3fthz49bK+hPzSm\nRg5IvCrKwjfp12YxmHrQON7+ntOCDkP6UFcb56IL9F4pNXXVcS5ZUA7dhKaznwDMbLqZPWJmL5jZ\n82b28cL6sWb2kJm9XPg7prDezOy7ZrbKzJ4zs+OG+kWIiIjIPlRA91N/+gkywD+5+xHAycBHzOwI\n4P8AD7v7HODhwn2AC4A5heUa4OZBj1pERERkD/tMatx9s7svK9xuA1YAU4E3Az8p7PYT4NLC7TcD\nt3neE8BoM5s86JGLiIhI/6lS052ZzQSOBZ4EJrr75sKmLcDEwu2pwPouh20orBMREREZMv1Oasys\nDvgN8Al339l1m+dPodqvnM3MrjGzJWa2pLGxcX8OFRERkf2lSk2emUXJJzQ/d/e7Cqu37u5WKvzd\nVli/EZje5fBphXXduPut7t7g7g319fUDjV9ERET2xaHb5cYHayky/Tn7yYAfAivc/ZtdNt0LXFW4\nfRVwT5f17ymcBXUy0Nqlm0pERERkSPRnnprTgCuBv5vZs4V1nwO+BtxpZh8A1gKXF7bdB1wIrAI6\ngPcNasQiIiKy3yrh2k/7TGrcfTH5a7L1ZmEv+zvwkQOMS0RERGS/aEZhERGRSlABlRpdpEdERETK\ngpIaERERKQvqfhIREakAlTBQWJUaERERKQuq1IiIiFSCIpwsb7CpUiMiIiJlQZUaERGRclek12oa\nbKrUyAHbtmEHy59YRVtze9ChVLRsNsdLz2/k5Rc2kcvlgg5HerFxww6W/309HR3JoEOR/bB+SzN/\nW7mR9s5U0KEcmAq4oKUqNTJgnbsSfPWD3+e5x14iEg2TTmW4+AML+OANbyV/yTAZLn9fuoYb/+WX\nJBNpAKpqYnzxm+/k8HnT93GkDIeWlnau/+yvWL1qK+FImGwmy1UfOJPLrzg56NBkL1p2dvAv//Fb\nXl7bSCQcIpPN8cG3ncK7Lz4x6NCkD6rUyIB9959+zt8WrySVSNPRliCdzPCHHy/i/p8tDjq0irKz\npYPrr/sZLTva6exI0dmRonn7Lj7/4dto35UIOjwBbvj8r3lp5WaSyQwd7UmSyQy3/WgRTz2xKujQ\nZC8++63f8eLqrSRTGdo7UyRTGX74m8d5bNnqoEMbEPPBX4qNkhoZkER7ksd+/wypRBrPZl9bEu1J\n7rrpj0GHV1Eevf/v+e4md8hm84s7uVyOxX98IejwKt7WLa28tHIL2XQOcuQXh0Rnml/98smgw5M+\nbG3ayQuvbCadzeHGa0tnMsPtf1gSdHjSB3U/yYB07Erkv0iz2e4bsllamtqCCapCtTa3k+pMdu/f\nzuVIdCRp1TinwLW2duSvCFxoHyOffwI0bd8VUFSyL61tCQjZ6z/9zfINZ9DYXKLtVoSVlcGmpEYG\nZMyEkXgm2+u2urqqYY6mso2rH9H7h1XOmTB51LDHI91Nnz6OVCJD11Fmu2+PHlUTREjSD9MnjyaZ\nyeaTmd0Kt0u23SogqVH3kwxIa9Mu+nqHtLd2DG8wFa55e++VMTNo3Nw6zNHInjZu3EE0Fu512069\nV4rWxm2txCK9t1tru8aqFStVamRAorFIn2c4VdXGhjmaylZVHSMaC5NOda+cRWMRqqqjAUUlu1XF\no4QsBPSsbNbWxoc/IOmXeDSChXr/jKutLr3PuGId2DvYVKmRAakdWc3Rp80lHOn+XyhWHeXCq84M\nKKrKdMZ5R/WZYJ5+zlHDHI3saer0sUycNIo9m6iqKsolb2kIJijZp+mTxjC1vpd2i0d46znzgwlK\n9klJjQzYv9zyQSbPnEB1XZyq2jjx6ijHnXkEb/vYeUGHVlHqJ43mk1+6jFg8QnVtnOqaGPGqKJ/+\n2uWMGV8XdHgVz8z40tfezthxddTUxKiqjhKLRTj73CM5+9wjgw5P9uLrn7yE8aPrqKmKUh2PEouG\nOffkwzj/tMODDm1gup7GNVhLkTH34OtRDQ0NvmSJTpErRe7O3//6ElvXbWf2vBkcfOS0oEOqWLt2\ndrLksZcxg4bT51KrAdtFJZvJsWzpqzTvaOeoedOZMnVM0CFJP2RzOZY8v47tze3MmzuF6ZMGr93M\nbKm7D0u5rmradJ/20U8N+uO+8n8+NWyvoT80pkYOiJkx77RD4bRDgw6l4tWNrGbBBfOCDkP6EI6E\nOOGkWUGHIfspHApx0tEzgw5D+klJjYiISAXQQGERERGREqFKjYiISCVQpUZERESkNKhSIyIiUu4q\nZPI9JTUiIiKVoAKSGnU/iYiISFlQpUZERKQSqFIjIiIiUhpUqREREakAlTBQWJUaERERKQtKakRE\nRKQsqPtJRESkEqj7SURERKQ0qFIjIiJS7ipkRmFVauSANW5o4oUnXmZXS3vQoVS0XC7HquUbeOX5\nDeRyuaDDkV5s2trC8pWb6Eykgg5F9sPG7a08t3ozncl00KEcGB+CpcioUiMDlmhP8NUr/5tlDy8n\nGo+QTma47KPn8/5/fQdmFnR4FWX5U6v56of/h0RHCnendmQ119/6fg6dPyPo0ARo2dnBZ7/2W1au\n3kY0EiKbzXHNP7yByy8+PujQZC9adnXyqVvuZcW6bUTCIbK5HB+++FSuPEfttj/MbA3QBmSBjLs3\n9LLPAuDbQBTY7u5nFtafD3wHCAM/cPev7e25VKmRAfvOdT/imT8tJ51M07Gzk3QyzT03PcgDP/lz\n0KFVlJ3N7Vz/nltobmyjsz1JoiNF05ZWPveum+nYlQg6PAE+/+/38MLLW0ilMrR3pEgkM9z6i7/w\n5DOvBh2a7MWnv/97lq/ZQjKdoT2RIpHKcPPv/sri5SXabsFWas5y9/l9JDSjgZuAS9z9SODthfVh\n4HvABcARwDvN7Ii9PYmSGhmQRHuCv9z1JKlE93JssiPJr775+4CiqkyLfvcMuWwOct2XbCbL4vv+\nFnR4FW9L405WrNpKJpcjGyW/hKEzmeH2e54OOjzpw9bmNp5bvZl0NkcuRH4x6ExluO2hJUGHV27+\nAbjL3dcBuPu2wvoTgVXuvtrdU8AdwJv39kBKamRAOtoSZLO9j9to3tY6zNFUth2NO0l19hyjkexI\n0ty4M4CIpKuWnR3kzMlFgZDllzDkYrC1aVfQ4UkfWnZ14uZ4CLDXFw/B1ubSazcjP1B4sBdgvJkt\n6bJc08vTO/CgmS3tY/tcYIyZPVrY5z2F9VOB9V3221BY1yeNqZEBGVU/As/1XnusqokPczSVraa2\nj39vhxGjaoY3GOlhysTRJMlB13FmZuBQXRcNLjDZqynjRpLM5fLZwG6F29XVarcutvfWpbSH0919\no5lNAB4ysxfdfVGX7RHgeGAhUA08bmZPDCQYVWpkQFob2wiHe//vk+zQmR3DqbM92ee2Np2RFrhN\n21qJRcM9Nxi0l/rZNGVsY9NO4pFe2g3oSJVouwU0psbdNxb+bgPuJt+t1NUG4AF3b3f37cAi4Bhg\nIzC9y37TCuv6pKRGBqRmZDUW6v2/z9hJo4Y5mso2dsJI4r38cqyqiTF2otoiaGNGVfe5bXL9yGGM\nRPbHmBE1fX5nTx47YlhjGRRD0PXUn3lvzKzWzEbsvg28EVi+x273AKebWcTMaoCTgBXA08AcMzvY\nzGLAFcC9e3u+fSY1ZvYjM9tmZsu7rLvBzDaa2bOF5cIu2z5rZqvMbKWZnbfvlyylqKomzlnvOIVY\nVfcv03hNnMv/+eKAoqpMZ1x8XK9Vs3AkzOkXzg8gIulq4riRzJs7lWikextVxSK86+ITAopK9mXi\n6DqOnz2VaLhnu111jtptP0wEFpvZ34CngD+4+/1mdq2ZXQvg7iuA+4HnCvv8wN2Xu3sGuA54gHyS\nc6e7P7+3J+tPpebHwPm9rP9W4fSs+e5+H0DhVKsrgCMLx9xUOCVLytB133kfJ7/pOKLxKDUjqolX\nx7j8ny7inHe9IejQKsqI0TV89fbrqJ8yhqqaGPHqGJMOGsfX7ryO6r7G28iwuvETF3PMYdOIRcPU\nVMeoqYry0SsXcMJRmkeomH39/W+iYc40YpEwtVUxauJRPnnpGZx6eIm2WwDdT4Uzl44pLEe6+42F\n9be4+y1d9vuGux/h7ke5+7e7rL/P3ee6+6zdx+7NPgcKu/siM5u579CB/KlWd7h7EnjVzFaR7zt7\nvJ/HSwmJV8f4/M8/RkvjTpo27WDKrElU11UFHVZFOvTYGfzkyRtYv2orZsa0WRM0AWIRGVlXxX99\n/u007thF884OZkwZSzym8zSK3ciaKm6+7q1sa9nFjl0dHDxxLPGo2q2YHUjrXFc47WoJ8E/u3kz+\nVKuuI5b3efqVlL7R9SMZrbEBgTMzDpozKegwZC/qx9ZRP7Yu6DBkP00YXceE0WXQbkV4WYPBNtCB\nwjcDs4D5wGbgP/f3Aczsmt3ntTc2Ng4wDBEREemPIAYKD7cBJTXuvtXds+6eA77P66dn9fv0K3e/\n1d0b3L2hvr5+IGGIiIiIvGZASY2ZTe5y9zJePz3rXuAKM4ub2cHAHPIjmUVERCRIAc1TM5z2OabG\nzG4HFpCfCnkD8H+BBWY2n/xLWgN8CMDdnzezO4EXgAzwEXfPDk3oIiIiIq/rz9lP7+xl9Q/3sv+N\nwD5PuxIREZFhUqSVlcGmc9NEREQqQDEO7B1sukyCiIiIlAVVakRERCqBKjUiIiIipUGVGhERkQqg\nMTUiIiIiJUKVGhERkUpQAZUaJTUiIiLlrkLmqVH3kxywdS9u5On7n6Fpc3PQoVS0dDrLs39bx7PP\nrSOT0UTexcbdeWntNp54bg2tbZ1BhyP95O6s3NjIYyvW0NKudit2qtTIgLW3tnP9JV/npaWvEIlG\nSCXTvPGqM/nY964mFFK+PJyeXvoqX7rxHtzzP8XC4RBfvv4y5h9zUMCRCUBj8y4+8Y272LitlXDI\nSGWyvOeiE7j6LacGHZrsRWPrLv7xlrtZv72FcChEKpPlvQsb+McLTsHMgg5vv1hhKXf65pEB+48P\n3MyLT75MsiNFe2sH6USaP/70L9x70wNBh1ZRmlvauf5Ld9HenqS9I0V7R4q2tgSf++KvaWtLBB2e\nAJ/+9j28urGJzkSKXR1JUuksP7tvCX9euiro0GQvPvXD3/HKliY6Uxl2JVKkMll++shSHn5O7Vas\nlNTIgHTu6uTx3y0lnc5BOPLakuxMcfd37ws6vIryp0dXkMs5uRB4YcmFIOvOn//yYtDhVbxN21pZ\ntW472Uzu9XENOSeRSHPHA8uCDk/6sGnHTl7csI1srvtAlM5Uhp//+ZmAojpAukq3SO8S7cn8/+dQ\nqFsZ1kNhWhvbAourErW0tJPMFsbQ7G4LdxKZLC2tGgMQtJ0dCXLZHNC9/O8O25r0XilWbZ1Jsru/\ntbs1HGxtLs12q4R5apTUyIDUjq7FMfbsVjYzIvFoMEFVqlCIXhoCgHBExdigjR1ZQzab6zGewYCo\nxp4VrbF11fkqTS8NF9H7qmipZWRA2nbsIhrtPSfO5Srg50Ax2cvov5yrLYLW1NJOPNb7e8XVPkVr\n+8524n18xpVss1VA95OSGhmQ0fUjiVX3XpE5ZN6MYY6mss06eALV1bEe66uroxwysz6AiKSraRNH\n95q8hELGkbMnBxCR9Me08aN7zV5CZhw9c1IAEUl/KKmRAQlHwlx5/duI18S7rY9Xx3jfly8PKKrK\ndPrJsxk7ppZI+PW3cyQSYmL9SE48/uAAIxOAEbVVXLbwGKr2qNbEoxGuuuSkgKKSfRlRHecdb+i9\n3T54zokBRXWAKqBSozE1MmBv+dgF1I6q5udf/S07trQw88hpXPP1d3HkqYcGHVpFiUTC3PzNd3PL\njx7l0cUrMYyzzziMD73/TMJh/W4pBh9/1wImjhvB7fctZWd7gqNmT+bj71rAjCljgw5N9uKTF5/B\nxFEj+MkjS2ntSHD0QZP450vP4JBJ44IObf95ZQwUtmLo021oaPAlS5YEHYaIiMiwMbOl7t4wHM9V\nM2G6z33Hpwb9cf/2358attfQH6rUiIiIVILgaxhDTrVpERERKQuq1IiIiFSAShhTo0qNiIiIlAVV\nakRERCpBBVRqlNSIiIhUAHU/iYiIiJQIVWpERETKXZHOADzYVKkRERGRsqBKjYiISCWogEqNkhoR\nEZEyZ2igsIiIiEjJUKVGDkg2m+OZv65i68ZmZh85lUOPnhZ0SBWruaWdJ5a8ihmccsIsRo2sDjok\n6SKdybJ4+as07ezg2NlTmDVlfNAhST+kMlkWvfwq29s7OG76FOZOLOF2q4BKjZIaGbDtW1v553ff\nys6WDrKZHBaCI+bP4Iab30Mspv9aw+m+h/7Ot255iFAoX3z9z5se4jMfPZ9zFhwecGQCsHpTE1d/\n81ek0lky2RwAZx87m3993/mEQhZwdNKX1Y07ePeP7ySZzpDN5TOChYfN4t/fcj7hkDo6ipFaRQbs\nG8L6fkcAACAASURBVJ+5k22bW+joSJFMZ0gk0jy/bA2/+v6fgw6tomze2sq3bvkjyVSWzkSazkSa\nZCrD1//rfpp27Ao6vIrn/v/bu+/4OOo7/+Ovz842dRfJtmwL27iBTTG4YHqvoQW4BNIgCSFw5HKQ\nHGmPX36XcLmDS+6XSyMQcsA5CZAQqgHHmFBMNVgGG1eMwU2uktXr7s58f3/syJalXVleSTur3c+T\nxzw0mpldvYevd/e73/nO92v41r0LqW9so6UtQkckRkckxisrN/H8O+u8jqeSMMZw658XUtvSRnMk\nSlssRlssxssffszTK4dmuYkxA75kGq3UqJS0NLWz9v1t2JaFCVgYv4UJ+Gm3DYufWO51vJzy6psf\nErVjGB8HLbZxWPrWRq/j5bwte+rYU9fUo+W/vSPGX19d5UkmdWhb9tWzo6ERI7i9bONLayzGo8uH\nYLmZQVoyjF4jUCmJxWxi4jabS5fmcx80tUS8CZWj6htasQ3xN90uoo5DQ2ObJ5nUAdGYTTRqAwcX\nkQH2NbZ6kkkdWtS2iTjxcutecDWtWm6ZSis1KiX+gIWRBH0BRMBvpT9QDnMwPSo0nUwu3MOZ4QKW\nD5OgiDq//KvMFLSseENEwoIbmiWXC28HevlJpSQasbH8if/5aCfh9CrIDyXc7hMhHA6mOY3qLhKz\nCQUSvybytXwyVrsdI+xPXG4FwUCa06i+0kqNSsmwEQWMrRjRY7vl93HquXrHTTqdPPtIwqGeb76B\ngMXJc470IJHqavLYUsIJKvrBgMWFc6d7kEj1xdSykeQlqLyE/BafOmaIllsO9KnRSo1K2b/c+Wny\n8oMEAvHLTaFwgOEjCvnSLed4nCy3TJ8yhgvPmkk4FH8DFoFwKMAVFx3PpIohPKZGlvBbPn785QsJ\nB/34rfhbbl4wQEXZMK475wSP06lkLJ+Pn155EeGAH797+3ZewE/F8BK+dNKJHqdLjZiBXzKNmAy4\nJWvOnDmmsrLS6xgqBTV7G1n0RCVVW2o45oQJnHfZLPILEl8OUYPHGMN7q7ex5NV1iAgXnj2TWTPH\nI0P02n822r63nide+4A9tU2ccsxELpg7PellKZU5ttXW85f3VrOzoZHTJ0/kU8dMJ5TkstThEpEV\nxpg5A/Jkh1BQWmFmXnr7gD/v8gXfTts59IVWapRSSikPpL1S86lBqNT8IbMqNXr5SSmllFJZ4ZCV\nGhF5UET2isiaLttGiMiLIvKR+3O4u11E5FcisklEPhCRoXnhUSmllMomg9CfJhP71PSlpeZ/gYu6\nbfse8JIxZirwkvs7wMXAVHe5Cbh3YGIqpZRSSvXukJUaY8xrQG23zVcAC9z1BcCVXbb/wcQtA4aJ\nSPlAhVVKKaVUinLglu5Uu3CPNsbsctd3A6Pd9XHA9i7HVbnbdqGUUkopTwiZeblooPW7o7CJ3z51\n2P+rROQmEakUkcrq6ur+xlBKKaVUjku1UrOn87KS+3Ovu30HUNHluPHuth6MMfcbY+YYY+aUlZWl\nGEMppZRSfWLMwC8ZJtVKzULgenf9euCZLtu/5N4FNR9o6HKZSimllFJq0ByyT42IPAqcBZSKSBXw\nr8DdwGMi8lVgK/AZ9/BFwCXAJqAV+PIgZFZKKaXUYcqFPjWHrNQYY65LsuvcBMca4Nb+hlJKKaXU\nAMrQu5UGmk48ovqlpqaJRc+tpGrbPo45djznX3gceflBr2PlHGMM763bzpK3NyACF506g1lHjfc6\nlupie209f12+ht2NTZw6ZQIXHzuN4ADNIaQGz/bGeh5dt5qdTY2cVjGBS6ccRVjLLWNpyaiUbVi/\nkztue5iYbRON2Lz1xkYeffhtfvv7rzB8eIHX8XLKz/73JRa9vpaOjhgAi99Yz1XnHc83P3+Wt8EU\nAK9t3Mxtjz5HzHaIOQ4vrfuYh96o5JGvX0d+MOB1PJXE0m2b+friZ7Adh6jjsGTzJu5/fzlPXfN5\nCgJD78ubOB79XZEtQBNgA7Huc0WJyFnE++Zudjc9aYy5sy+P7U7nflIp++l/PEtbW4RoxAagvT3K\nvpomFjyw1ONkuWXD5j08v3QNbR0xHAFHoC0S4/ElK9lctc/reDnPdhy+9/hi2qIxoo6DAVqjUbbU\n1PGnt9/3Op5KwnYcbv/7Itpj8XIDaI1F2dJQx0OrVnicbkg62xgzq5dKyevu/lmdFZrDeOx+WqlR\nKamvb2FHVZeBpkUAcBzD0lfXe5QqN735/se02447upa4C0QchzdXfux1vJy3cU8NLR2RAxviLxU6\nbIdnV+lrJVN9VLeP5kjHgQ1uuUUch6c2DtFyy4ERhbVSo1Li91vYjjnwIQr7f0bclhuVHtX1rfGV\nznJw1w1Q29jmSSZ1gCVCxOmsdLob3Z+NbR3JHqY8ZomPiElSbpF2r2L1i4cTWhpgiYisEJGbkhxz\nsoisEpG/icjMw3zsftqnRqUk0hFDJMHYS10/WFVahIJW0n3BoL7EvRa1HXwCTo/XCvh8+nrJVBHH\nxofgdG+OEPDp+1xXpSJS2eX3+40x93c75jRjzA4RGQW8KCIb3HklO70HTDDGNIvIJcDTxCfG7stj\nD6ItNSoleflBLCvxh2nZqOI0p8ltFWOG47d6vpSDAYtxo0o8SKS6Gl6Qh9+X+LUyqWxEmtOovhoR\nzsPvS/wReeSwIVhuhsEaUbimc3YAd+leocEYs8P9uRd4CpjXbX+jMabZXV8EBESktC+P7U4rNSol\neXlBzjz7KILdWgnC4QCfvW6+R6ly0/knTSfg7/mhGfBbnDN3mgeJVFdjhxVz3PgxPSqeeQE/Xzl1\ntkep1KGUFxZx4pixBLpVbPL8AW6aNdejVEOPiBSISFHnOnABsKbbMWNE4s1fIjKPeN1kX18e251W\nalTKbvv2xZw4exLBoEVBQYhg0M+VV83mokuO9zpaTikpyuMXd1zFyJJ88sMB8kIByoYX8uvvXk1B\n3tC77TQb/eq6yzhu3BhCfj+FoSDhgJ9/Pu9UTps60etoqhf3Xng5J4wuJ2z5KQwGyfP7+fa8Uzl7\nwpFeR0uJR31qRgNviMgq4F3geWPMYhG5WURudo+5BljjHvMr4Fp3MN+Ej+39HDNgQqo5c+aYysrK\nQx+oMlJNdRN79zZyxBEjKSwKex0nZzmO4aNt1YjAlIoy7a+RgarqGqhpbmXa6FIdn2YI2d5YT3Vr\nK0eNLCV/AMenEZEVfblNeSAUDq8ws8755wF/3jefvCNt59AX2otQ9VtpWRGlZUVex8h5Pp8wfeIo\nr2OoXowfXsL44drPaaipKB5GRfEwr2P0n/dtGINOKzVKKaVUlhNyY0JL7VOjlFJKqaygLTVKKaVU\ntjtwC3ZW05YapZRSSmUFbalRSimlckAu9KnRSo1SSimVC3KgUqOXn5RSSimVFbSlRimllMoBevlJ\nqUN4/d1NPPSXt9hT08T0yaP5+udPZ/rk0V7Hyjkt7RHu/fsyFq3cAAiXzz6am849SUetzRDGGJ5c\nu5bfVVZS29bGSePG8S+nn86k4cO9jqZ6YYzhiU1ruG/1u+xra2XemAq+M+d0JpeM9DqaSkIrNSpl\nz/79A37xwMu0d8RA4J2VW/hgfRW/+cm1HDV5jNfxcobtOHzx3r+weU8tEccB4A+vrWDZR9t45BvX\n6XQJGeAXb7/NA5WVtMZiALzw8Sbe2LaN57/4RcaX6AjDmeqXK9/ivtXv0haLAPDC1o28sXMLf7vi\nBo4YaiMMG8DJ/qYa7VOjUmLbDr9dsJRWJ4YTAicITghaYzF+96fXvY6XU17fsJmt1XX7KzQAHbbD\nR7treOfjbR4mUwDNkQj3L1++v0ID8c+W5miE+95918Nkqjct0Qi/XbWMNjviDscLRgwtsQ5+88Hb\nXsdLjRmEJcNopUalpK6hlaZYJP4vSGT/Yvyw6qMdXsfLKZWbd9Aes/e/8XYu7bbN+1u0LLz2SW0t\nMeNWOLuUjzGwdOsWD5Op3nzSUEsMGzjoLQ4DvLZjs7fhVFJ6+UmlJBCw4i936XZpQwRbr3akVU1T\na/yDMoHqptb0hlE9iIBtTM8yEmiLxhI+RnnPJ4KDSfQWR7s9NMstFzoKa0uNSknMdvBZiT9J/QEr\nzWly2+iSgqT7xpTo7OneE/yS+K22IBhMcxZ1OKxk5RbQDviZSis1KiUjSvIZmeDDVICTjp+Y9jy5\n7OSpEwgHeja6hgN+TppS4UEi1dW0kSMJ+ntW9C0RLpo6xYNEqi8ml4wkZCUut4snTPMg0QDonP9p\nIJcMo5UalRIR4Y6vnEc46N/fqu63fOTnBbnlM6d5mi3XnDS5ghMmjj2oYpMX8DN/yhEcf0S5h8kU\nQMjv5/tnnEHYf6B8Aj4fw/Py+NqcuR4mU70J+/38cO655FkHl9uwUB43HXOSh8lUb7RPjUrZ6bMn\nc88PP8sfF75L1Z56jps2li9cNo/ysmKvo+UUEeHeL1/Jk8vX8FTlOkTg6rnHcOXsmUj3DgHKE58/\n7ngmDRvO71dUsqe5mTMmTuTG2XMozc/3OprqxXXTj2dC8TB+v/Zddrc0cca4Sdw4cx5leckv+Way\nXOhTIyYDmo/mzJljKisrvY6hlFJKpY2IrDDGzEnH3yoqHm/mzP+nAX/eV1/8XtrOoS/08pNSSiml\nsoJeflJKKaWynACSAVdmBpu21CillFIqK2hLjVJKKZULnEMfMtRppUYppZTKAXr5SSmllFJqiNBK\njeq36rpm1ny8i+bWDq+j5DTHMWzYVc2Hu6vJhKEaVE9VjQ2s3L2LtmjU6yjqMOxoqWdV7Q7aYkO4\n3AZjhu4MfJvRy08qZe0dUf7PfYtYtmYLAb9FNGbzuQtnc8vVp+qgb2n2/rad3Pboc7R0RAAoCof4\n9ecv45hxYzxOpgDq2tq4edEzrNqzh4Dlw3Yc7jjldL58/IleR1O9qI+0cetbj/FB7U4CPgvbONx+\nzNncMFVHFM5U2lKjUnbXgr/z1gebiURtWtoiRKI2j76wgmdfX+t1tJxS39rOjQ89wd6mFloiUVoi\nUXY3NvPlBx7fX8lR3rp50UJW7NpJhx2jORKhLRbjp2++xmvbtngdTfXim28/zns1VbTbMZqiHbTG\novx89css3bXJ62gpGIR5nzKwRVgrNSol7R1Rliz7kJh9cHf6jqjNQ88u8yhVbvrbBxtoj8Xiv4i7\nAG2xGItXb/Qsl4rb0dTIil07sDs/ANzyabdt7lmur5VMtbu1keXV24iZzve4eMG12THu3fCGd8H6\nQczAL5lGKzUqJU2tHdhO4vsD99Q2pzlNbltVtQvHsP/DEuLrtjGs3bnHq1jKtbu5GRtzUIWz8+dH\ntfu8iqUOYU97EzHT+cI6uOA+bqzxKpY6BO1To1LSW5cZ7U6TXgYOrtCoDJPk62zXz0qVeZK2Qkgm\nXnXpmyEbvO+0pUalpCAvhN9K/M+nbHhhmtPktuMryvElqEn6RJg5brQHiVRXYwqL8PsSv1amjihN\ncxrVV6PyivBL4nKbUqzllqm0UqNSkhcKcN68aT0qNqGgny9fqncGpNPFx04nHOjZ6JoXDHDRMdM8\nSKS6GldUzIljyrG6VTzDfj//OFtfK5mqPL+Y2aUVWN0qNmHLz81Hn+ZRqn4wIM7AL5mmX5UaEdki\nIqtFZKWIVLrbRojIiyLykftz+MBEVZnm+zecz/xjJxIMWBTkBQkGLD573glcfsYxXkfLKcPz87j/\nS5+mtDCf/GCA/GCAUUUFPHjD1RSEgl7HU8C9F1/OCWPKCVl+CoNB8vx+7ph/GmdOmOh1NNWLX598\nNSeMHBcvN3+IPCvAbTPP4qzyKV5HU0lIfwbpEpEtwBxjTE2XbT8Fao0xd4vI94Dhxpjv9vY8c+bM\nMZWVlSnnUN7aU9vE3tomJpaPoKgg7HWcnOU4hg27qxGB6aPL8Pm0w0am2dZQz762VqaPLCM/EPA6\njuqj7S111LS3MK1kFAX+gfuiICIrjDFzBuwJe1FcOM6cdPwtA/68f3/rh2k7h74YjI7CVwBnuesL\ngFeBXis1amgbPaKI0SOKvI6R83w+YcbYUV7HUL04omQYR5QM8zqGOkwVBcOpKMiCiw7Z30+4331q\nDLBERFaIyE3uttHGmF3u+m4gYU9FEblJRCpFpLK6urqfMZRSSimV6/rbUnOaMWaHiIwCXhSRDV13\nGmOMSOLheYwx9wP3Q/zyUz9zKKWUUqoXOkv3IRhjdrg/9wJPAfOAPSJSDuD+3NvfkEoppZRSh5Jy\npUZECkSkqHMduABYAywErncPux54pr8hlVJKKdVPOTD3U38uP40GnnJnY/YDjxhjFovIcuAxEfkq\nsBX4TP9jqky1p76Jx978gC176zjxyHFccdIMCsMhr2PlHGMMb27dxtPr1uMT+PTMGcyvqNDZ0jPI\n5sZaHv5wJTtbGzlr7JFcfuQMwpYO6p7pqlprWLjjLfa21zN35HTOG30iIWsI3rlmgAwcV2ag9euW\n7oGit3QPTR9s2cVN9zxOxHGIOQ4hy2JYQR6PfvtzlBYXeB0vp/zghRdZuH49be7ElmG/xbXHHccP\nzznb42QK4KWqTfzj0qeJGRsHh5AvQEXBMJ665IsUBvRLQKZaVrOe/7t6AbaJ4RhD0BdgdHgE9879\nJgX+/g9fkdZbugvGmfkzvz7gz/vi8n/NqFu6dURhlbIf/OlvtNgxosbBCLQ7Nnuamvn18296HS2n\nfLB7N0+sXUubHds/n1C7bfPwqlVsrNGJ97wWcxxuf+NZor4IxrIRyxCRCJtbanhovX6Zy1S2cfjJ\n2oeJmQgGBxFD1ETY0b6Xv259zet4h00wiBn4JdNopUalpLa5lW11DfFf5MBigMUrP/QwWe55bsOH\nxIxzUDkgEDUOz2/QsvDah/XVtJp2ID7Za+fiiMOfN630OJ1KZkvzbtqcNuDgcjPGYdGuZR6nU8no\nBV2VkmjMTjw7tECHbXuQKHdtqa9LOtvzlvr69IZRPdR2tGAwPWavF4Emu82bUOqQGqItkKTcWoZq\nuWVgy8pA00qNSknAbyEkHqAymGByRTV4Jg1PPtLppBFZMArqEDcyXICIYBK8WkqCOq1IpioJFiAk\nLrfCwBAttxyo1OjlJ5WSEYX5VJSW9NguwEWzdGbodLr0qKMIWlaP7QGfj0umaVl4bVpJGUWBnvMF\n+RCunXKCB4lUX0wsGENhgs7APoRLy0/2IJHqC63UqJTd9bmLyQ8G8Pvi/4xCfotRJYV885JTPU6W\nW44dPZqrZswgz3+ghSzP7+cLs2YxrbTUw2QKwO/z8fOTryBs+bHc64Rhy8/k4pHcMG2ux+lUMpb4\n+P6MLxDyBbDcj8qQL8D4/FFcXXGGx+lS0HlL90AvGUZv6Vb9sru+iceXrY6PUzNpHFfMnUFBeOBm\nsVV9Y4zhre3beWbdOkSET8+YwUnjx+s4NRlkS1Mtj256n52tjZxZPpnLJswgpOPUZLydbTU8u+Nt\n9rbXMXfkUZwz6gSCAzROTTpv6S7JH2vmT//agD/vkpV3ZtQt3VqpUUoppTyQ7krNydNuHPDnfWHV\nv2VUpUYvPymllFIqK2jbp1JKKZULMuDKzGDTlhrVbx2xGHuamok5GdhrLMfUdbRS3zFEx9DIAW12\nhJqORhyjr5WhJOK00xStxQzpchuEySwzsJKkLTUqZTHH4a5XlvLnlasxxOcb+s5Zp3Pt8cd5HS3n\nfNywj9veWsiGuj0AzBwxhl+cejkTi0Z4nEwBtNsRfrb+SV7asxJBKA7k863pV3Lm6GO9jqZ6EXU6\neH7nb1nb8DoAYauQi8tvZkbJKR4nU8loS41K2V0vL+XhylVEOmyiHTZNLRHufOEVlmzc5HW0nNIS\njXDVCwtYs283MccQcwyranZx1QsL6LBjXsdTwI9WP8JLe97DmBiOiVIfaeDHax5hTcNWr6OpXjy1\n/f+xtuFVjIlgTIS2WC1PV/0X21rWeR3t8BlyoqVGKzUqJR2xGA+vWIVtm4OmHIrGHO5+eehN9jaU\nPbN5LY2Rjh7b69vbWbR1vQeJVFf7OppYtm8dxjgHzSFkmyi/+2iR1/FUEi2xej5qXoYx9sFzdpkI\nL+9Z4HU8lYRWalRKalpacNwKTVcC7Kxv9CJSzlq66+OE2w2wdNfm9IZRPXzSvBNweswhBLCpaUfa\n86i+2du+jWTltrf9k7TnGRA5MPie9qlRKfFJ8vqwL9nsimpQBH3JX8YhX8/pE1R6BZKUjwhYPn2t\nZCq/JH7tiPT+/pfJJAMvFw20oVkyynNlhQWE/InfrKeV6dD86XRhxbSE1UgfwgUVOveT1yYXjsWX\n5K32xOGT05xG9VVZeCKSpNyOyD8mzWlUX2mlRqXE7/PxrTNP6TGRYtCy+OEFZ3kTKkddUDGd8YXD\nDmoh8yFMKh7BWWP1Q9NrRYE8Pj3+lB7f/EO+ADdOvsSjVOpQwlYBc0dchiUHf3nzS5BzxtzgTaj+\nyoGOwnr5SaXshnknUhQOcc8b71DT3MLUspF899wzmF0xzutoOSVoWTx90fX8x3svs3jbh4jApROO\n5nsnnI3l0+8tmeDWaZdTFh7GX7e9RlOslaOLj+DWqZdzRMEor6OpXpw35qsUB8pYtu9J2u0mxuZN\n57wxN1IaqvA6mkpC535SSimlPJDWuZ/C5eaUCdcP+PMu3vifhzwHEdkCNAE2EOt+vIicBTwDdN7Z\n8KQx5k5330XALwEL+B9jzN29/S1tqVFKKaWynueXi842xtT0sv91Y8ylXTeIiAXcA5wPVAHLRWSh\nMSbpQEFaqVH9sq2xnj+sfZ+P62uZVz6ezx19PCWhsNexco4xhjeqN7B45/sg8Kmxszm5dBqS6H5U\n5Yldbdt5vXoJ9dF9HF08i3kjziBk6Wsl0zVEPmFj/eO0xHZTnj+fI4svJeDL9zpWrpgHbDLGfAIg\nIn8GrgC0UqMG3vLdVXz++ceIOjbGwNKqzdy/6l3+ds0NjCko8jpezjDG8KPVf+G1vatwiALwVvVq\nzh8zhx8cc7XH6RTAyrp3+NPWX+MQRYCNTSt5Ze9z3HHU3eRZ+gGZqaqal/Lm7h9giACwp/VtNtQ9\nwsVH/JGgNQTf47xrqTHAEhExwO+MMfcnOOZkEVkF7AT+xRizFhgHbO9yTBVwUm9/SHsRqpQYY7jl\nxWeI2PEKDQKOMdRF2vnRmy95HS+nrK7fxtLqFRiJ4PMZfD6DkQhL9ixjY+NOr+PlPNvEeHjbPRii\n+NxRaQ02DdE9LNn1lNfxVBKOifHW7h9iiOwfMd0Qo83eweraB7yOl0lKRaSyy3JTgmNOM8acCFwM\n3CoiZ3Tb/x4wwRhzPPBr4OlUw2ilRqWkuq2FmvbWA/MjQOernpe2Jx7hVg2OhTuW0X3k0/i6w7M7\n3vEoleq0veUTDB0Jygcq6171JJM6tNqODRjaDhoDqnN9a+NzXkTqv8G5pbvGGDOny9KjFcYYs8P9\nuRd4ivhlpa77G40xze76IiAgIqXADqDrrWbj3W1JaaVGpaQ1Gk28Q8B2vL+jLpfURRqS7qvtZZ9K\nj2a7Id743o0IRE3PObtUZuiw6xNuF8CmPb1hhjARKRCRos514AJgTbdjxojbAVBE5hGvm+wDlgNT\nRWSSiASBa4GFvf097VOjUjK6oBAfiaf+GBHOS3ecnDZ7xBRW1H2YdJ/y1vi8SW7TTM+aTVloTPoD\nqT4ZHprG/ubnbgr949Oep98M4M0XztHAU26dxQ88YoxZLCI3Axhj7gOuAW4RkRjQBlxr4uPNxETk\nG8ALxG/pftDta5OUVmpUSvL8AT515FE898mGg17ylgjfmXu6Z7ly0WXj5rNgyxIizsGtZ3lWkIvL\n53qUSnUaFixlQv4UtrZ+dNB2HxaXln/Bo1TqUPL9oygNH0dN+6qDtgsWs0q/6VGq/jBg0j8DpXvn\n0vEJtt/XZf03wG+SPH4R0Ofp7PXyk0rZz868iAsnTiXgswhbfoKWxa2z5vMP04/1OlpOKQrk898n\n3EJpqISg+AmIn9Gh4fxy9q3k+UNex1PAVyd9j8kFM7HET0CCBCTEleNuYHrxcV5HU704vfznjMqb\ni48AloSwJMwJpd+ivOAUr6OpJHREYdVvldXbWV+7h9PKJzGpeKTXcXJWxI7yWvV7iAhnlJ5IwNKG\n2ExijOGT5pXsi+zkqOL5FAf0tTJU1Le9SWt0C6V55xAMlA/Y86Z1ROHQaHNK+ecG/HkXb/1F2s6h\nL/RdT6WsKdrOjW8+zJq6nQjCXWsNV02YxZ0nXopPB31Lq5V16/jZh/cTdWwAHtryKN8/6hZmlugs\n3ZmgObqPx7Z9l4bIDkB4c+89zB35WU4bNfDD1quBE7P3UlV9DbHYZgTY1igMK7yV0mHf9TqaSkIv\nP6mU/dOyx1hVu4OYMcSMg43h8a3v8z8b3/Q6Wk5piDTx7+t/S7sTwXb/a7M7+PHaX9ESa/U6ngIe\n23oH9ZFtmPjUNxhiLN/3CBsalnodTfViR/U/YMc+Qoi5S5SG5l/S2DoExxfq7Cg80EuG0UqNSklL\nLMKy6i1Aj2FquP/DNzxKlZue2fkiToL70GwcFu16Nf2B1EFqO3bSEK1KMKST4Y29D3qYTPUmEt1K\nLLaxR7mBobb+Ls9y9cvgjFOTUbRSo1KyqzXx+CdCvMKj0ufj5m1J921s2px0n0qPve2Jb7cXoMXe\nl94wqs8i0Q8SbhfAcfakN4zqM63UqJSMChcm3Re2AmlMosbnJx/r5Ij8sWlMohIpDU1Mui/PKk5f\nEHVYgoHpSff5fMPTmGQAaUuNUokVB/OYMSzxh+l1R85Oc5rcdsXY8xF6dsz2IVw29lwPEqmuRoYm\nUmCNSLBHOKl04O9GUQMjGJiG31eeYOg9YXjRUBynJjdopUal7N6Tr6UkECbekyb+0j+6ZDS3zdQP\n0nQaFR7JVyZds/96PxgEuPnIzzEsqC0BXhMRrpnwnwQkgOC4i+HIghM4btinvI6nelFe9hg+K78X\nygAAC2pJREFUDh4hPS94BsWFN3gTqF8GoZVGW2pUNnn4k7dplyYCfhu/ZRPwR9ncVsXymk+8jpZT\nYk6MZ3c8Rb6vg5DECEmMfF8HT+74K7axvY6ngE11v6Lcv5MyfyMjrGbKA7WY6BIaIx8d+sHKM5Hm\nn1MqMUrEokgsRoifAnsZseh6r6MdPgM4zsAvGUYrNSolUcfmj1vit25blsHvN1hW/HXzo1Upzxqv\nUvDSnldodVoQHwQsh4DlID5otpt4vfotr+PlvI5YA1UtLyMCeb4ohVYHAXFwcHh/74+9jqeSsO16\nTNvz+HxC2GeR77MI+Hz4jEN7/R1ex1NJaKVGpWR7Sy3GGLqPsScCNZEmb0LlqPfrVyZuBTbwXt17\nac+jDlbdvizJHqEhuimtWVTf2R1v9Xh/g/jlROwh2sKml5+USqw4ECZB31QALNF/VulU5C9K+OaL\nQHFA+9R4LWyNSrrPEp2bK1P5/KMTTdANgGi5ZSz99FEpKQ0XUeTv7CTclWHeyIkeJMpdl469hMTv\nvoZLyy9OdxzVzcjwLHxYCfYYKgouTXse1TdW4EREEg9P4Q9/Os1pBoi21CiVmDGGqGnv/K3LAns7\n6ryKlZPqIlsptto4uBwMJf426qJV3oZTOCZCWHqWj4VD1N7obTiVlDERQgmaowUhYCcf8FJ5a9Am\ntBSRi4BfAhbwP8aYuwfrb6n029ayDwebgA8cE3/hi8RvJd7eWu1tuByzou5ligPt5FlR2pwAQrxD\nqt9ns3zfixxdnDET6OakmtalWOJQSAdRLAxguZWa+vZKr+OpZCKvIQhhgtg4GAw+fPgQiK7wOl0K\nTEbO1TTQBqVSIyIWcA9wPlAFLBeRhcaYdYPx91T6hXx+wCAiWNLzEpRKn4jdDEDA5xDwdRy0L+ro\nhJZeE4J0vlaCdL/FPupFJNUHxsQvr4sI/m6XDx0T8yZUfxgwJvNuwR5og3X5aR6wyRjziTEmAvwZ\nuGKQ/pbygCMxfHLgktMBBr9Px0ZJJ78kf6OyRMvCa0J7kj3x1hqVmYxpS7LdJJxAVmWGwarUjAO2\nd/m9yt22n4jcJCKVIlJZXa2XK4YaAcL+zm+ZZv9PwZDn1w/SdCrwF+LHpnufDb/Y5PuTz9Gl0sMn\nAbeFpuuXgPhrJSCJOhCrjODzE8XGGINxO8QaYzAY7KHaHdUxA79kGM9KxhhzvzFmjjFmTllZmVcx\nVIrK80opDAQoDHYQ8scI+GKE/VEKghGOLjnC63g55aTSKwlYDmFfDD8OfuLrAZ/DyaVXeR0v5w3P\nOwuf+AgRw8LBwiaATRCb0vwLvI6nkvAFz8LgI0IMG4eYsYlhE8XGFzrf63gqicGq1OwAKrr8Pt7d\nprLIT469EZ8IQcsmHIgRsBzy/EHuPParXkfLKZMLT2Bq4XxEDEHLJmjZiBhmFJ9JRf5RXsfLeT6f\nxbTSnyICAXEIiIMlhqA1kqml/+F1PJWEz+cjUPLfANg42Dg4GJARBIb9p8fpUpQDt3QP1t1Py4Gp\nIjKJeGXmWkCno80ys4ZP4enT/o0FW16gqnUvs4ZP4epxZ+K3Bu2mOpXEZyf8H7a2rOWdmqcRgfml\nV2uFJoOMLryCYeH5bK3/JR2x3ZTmX8CYos8gOlBlRvPnX4YvdBKxpv/GsXfhD5+PlX/d0Cw3YzJy\nrqaBNiifPsaYmIh8A3iB+C3dDxpj1g7G31LeKgzkc+vUIToQVZaZUDCTCQUzvY6hkgj5RzNNW2aG\nHJ81iuCwu7yOofpo0L5SG2MWAYsG6/mVUkopdRgy8HLRQBuCbWhKKaWUUj1p5wellFIqBxjtU6OU\nUkqpoS8z71YaaHr5SSmllFJZQVtqlFJKqWxnyMgRgAeattQopZRSKitoS41SSimVC3SWbqWUUkqp\noUFbapRSSqksZwCTA31qtFKjlFJKZTtj9PKTUkoppdRQoS01SimlVA7IhctP2lKjlFJKqaygLTVK\nKaVULsiBPjViMmAuCBGpBrZ6nWOAlQI1XocYZHqO2UHPMTvoOQ49E4wxZen4QyKymPj/v4FWY4y5\naBCeNyUZUanJRiJSaYyZ43WOwaTnmB30HLODnqNS2qdGKaWUUllCKzVKKaWUygpaqRk893sdIA30\nHLODnmN20HNUOU/71CillFIqK2hLjVJKKaWyglZqUiAiFSLyioisE5G1IvLPXfb9k4hscLf/tMv2\n74vIJhH5UEQu9CZ53yU7RxGZJSLLRGSliFSKyDx3u4jIr9xz/EBETvT2DA5NRMIi8q6IrHLP8cfu\n9kki8o57Ln8RkaC7PeT+vsndP9HL/H3Ryzk+7P5bXCMiD4pIwN2eNeXYZf+vRKS5y+/ZVI4iIv8u\nIhtFZL2IfLPL9qwoRxE5V0Tec99z3hCRKe72IVeOKg2MMboc5gKUAye660XARmAGcDbwdyDk7hvl\n/pwBrAJCwCTgY8Dy+jxSPMclwMXu9kuAV7us/w0QYD7wjtfn0IdzFKDQXQ8A77jZHwOudbffB9zi\nrv8jcJ+7fi3wF6/PoR/neIm7T4BHu5xj1pSj+/sc4I9Ac5fjs6kcvwz8AfC5+zrfc7KmHN33nqO7\nlN3/DtVy1GXwF22pSYExZpcx5j13vQlYD4wDbgHuNsZ0uPv2ug+5AvizMabDGLMZ2ATMS3/yvuvl\nHA1Q7B5WAux0168A/mDilgHDRKQ8zbEPi5u18xt8wF0McA7wuLt9AXClu36F+zvu/nNFRNIUNyXJ\nztEYs8jdZ4B3gfHuMVlTjiJiAT8DvtPtIVlTjsTfc+40Jj5UbLf3nKwoR3p/zxlS5agGn1Zq+slt\n8jyB+LeKacDpblPoUhGZ6x42Dtje5WFV7rYhods53gb8TES2A/8FfN89bEieo4hYIrIS2Au8SLwV\nrd4YE3MP6Xoe+8/R3d8AjExv4sPX/RyNMe902RcAvggsdjdlRTm65/gNYKExZle3w7OpHCcDn3Uv\nBf9NRKa6h2dTOd4ILBKRKuL/Vu92Dx+S5agGl1Zq+kFECoEngNuMMY3E59IaQbzJ9A7gsaH+zSHB\nOd4C3G6MqQBuBx7wMl9/GWNsY8ws4i0V84CjPI404Lqfo4gc02X3b4HXjDGve5NuYCQ4xzOAfwB+\n7W2ygZOkHENAu4mPsvt74EEvM/ZXknO8HbjEGDMeeAj4uZcZVWbTSk2K3G+4TwAPG2OedDdXAU+6\nzajvAg7xuTZ2ABVdHj7e3ZbRkpzj9UDn+l85cBltSJ5jJ2NMPfAKcDLxpvrOyV67nsf+c3T3lwD7\n0hw1ZV3O8SIAEflXoAz4VpfDsqUczwamAJtEZAuQLyKb3MOyqRyrOPB6fAo4zl3PlnK8GDi+S+vi\nX4BT3PUhXY5qcGilJgVu68sDwHpjTNdvDU8TfzNFRKYBQeKTry0ErnV7608CphLvx5CxejnHncCZ\n7vo5wEfu+kLgS+5dF/OBhgTN/hlFRMpEZJi7ngecT7zv0CvANe5h1wPPuOsL3d9x97/s9knJWEnO\ncYOI3AhcCFzX2R/DlS3luMIYM8YYM9EYMxFoNcZMcR+SNeVIl/cc4q/Lje56tpTjeqDEfT+lyzYY\nguWoBp//0IeoBE4lfm13tXv9F+AHxJt+HxSRNUAEuN59ka0VkceAdUAMuNUYY3uQ+3AkO8evAb90\nvxm1Aze5+xYRv+NiE9BK/K6MTFcOLHA7lPqAx4wxz4nIOuDPIvIT4H0OXGJ7APij+42/lvgdF5ku\n2TnGgK3A2+4V0ieNMXeSReXYy/HZVI5vAA+LyO1AM/H+J5BF5SgiXwOeEBEHqAO+4h4/FMtRDTId\nUVgppZRSWUEvPymllFIqK2ilRimllFJZQSs1SimllMoKWqlRSimlVFbQSo1SSimlsoJWapRSSimV\nFbRSo5RSSqmsoJUapZRSSmWF/w+jtD4eGScQBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0adadaef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(x,y,c=z)\n",
    "plt.title('log loss')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:37:27.128137Z",
     "start_time": "2017-11-18T00:37:26.963641Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid RGBA argument: -0.93098905452915248",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Not in cache, or unhashable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: (-0.93098905452915248, None)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-421-03316127cc9a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log std'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3432\u001b[0m                          \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3433\u001b[0m                          \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3434\u001b[0;31m                          edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[1;32m   3435\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3436\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1896\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1897\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1899\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m   4032\u001b[0m                 \u001b[0moffsets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4033\u001b[0m                 \u001b[0mtransOffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'transform'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4034\u001b[0;31m                 \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4035\u001b[0m                 )\n\u001b[1;32m   4036\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIdentityTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, paths, sizes, **kwargs)\u001b[0m\n\u001b[1;32m    900\u001b[0m         \"\"\"\n\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 902\u001b[0;31m         \u001b[0mCollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    903\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_paths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, edgecolors, facecolors, linewidths, linestyles, antialiaseds, offsets, transOffset, norm, cmap, pickradius, hatch, urls, offset_position, zorder, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hatch_color\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hatch.color'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfacecolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linewidth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mset_facecolor\u001b[0;34m(self, c)\u001b[0m\n\u001b[1;32m    679\u001b[0m         \"\"\"\n\u001b[1;32m    680\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_facecolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_facecolors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m         \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Not in cache, or unhashable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_rgba_no_colorcycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0;31m# float)` and `np.array(...).astype(float)` all convert \"0.5\" to 0.5.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0;31m# Test dimensionality to reject single floats.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid RGBA argument: {!r}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_c\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m     \u001b[0;31m# Return a tuple to prevent the cached value from being modified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Invalid RGBA argument: -0.93098905452915248"
     ]
    },
    {
     "data": {
      "image/png": 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/teqJAgCso2XObF2U5Gh3P9DdTyW5NclVWwd0953d/cTi7l1J9q92mgAA62mZ2DonyYNb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ad9ae1080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title('log std')\n",
    "plt.scatter(x,y,c=np.log(v))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:37:28.641120Z",
     "start_time": "2017-11-18T00:37:28.435562Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid RGBA argument: 0.39416366815567017",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Not in cache, or unhashable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: (0.39416366815567017, None)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-422-0554dbdab3a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'std'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3432\u001b[0m                          \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3433\u001b[0m                          \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3434\u001b[0;31m                          edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[1;32m   3435\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3436\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1896\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1897\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1899\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m   4032\u001b[0m                 \u001b[0moffsets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4033\u001b[0m                 \u001b[0mtransOffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'transform'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4034\u001b[0;31m                 \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4035\u001b[0m                 )\n\u001b[1;32m   4036\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIdentityTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, paths, sizes, **kwargs)\u001b[0m\n\u001b[1;32m    900\u001b[0m         \"\"\"\n\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 902\u001b[0;31m         \u001b[0mCollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    903\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_paths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, edgecolors, facecolors, linewidths, linestyles, antialiaseds, offsets, transOffset, norm, cmap, pickradius, hatch, urls, offset_position, zorder, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hatch_color\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hatch.color'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfacecolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linewidth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mset_facecolor\u001b[0;34m(self, c)\u001b[0m\n\u001b[1;32m    679\u001b[0m         \"\"\"\n\u001b[1;32m    680\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_facecolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_facecolors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m_set_facecolor\u001b[0;34m(self, c)\u001b[0m\n\u001b[1;32m    664\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 666\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_facecolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    667\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m         \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Not in cache, or unhashable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_rgba_no_colorcycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0;31m# float)` and `np.array(...).astype(float)` all convert \"0.5\" to 0.5.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0;31m# Test dimensionality to reject single floats.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid RGBA argument: {!r}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_c\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m     \u001b[0;31m# Return a tuple to prevent the cached value from being modified.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Invalid RGBA argument: 0.39416366815567017"
     ]
    },
    {
     "data": {
      "image/png": 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sAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAg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\ncAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ad8a6f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title('std')\n",
    "plt.scatter(x,y,c=v)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:17:03.298436Z",
     "start_time": "2017-11-18T00:17:03.294472Z"
    }
   },
   "outputs": [],
   "source": [
    "# save\n",
    "pickle.dump('points267x266', open(points_file,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T14:52:41.348966Z",
     "start_time": "2017-11-17T14:52:41.249386Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save\n",
    "\n",
    "pickle.dump(points, open(points_file,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T09:04:53.552465Z",
     "start_time": "2017-11-17T09:04:53.434478Z"
    }
   },
   "outputs": [],
   "source": [
    "# save\n",
    "points = pickle.load(open(points_file,'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:16:53.141578Z",
     "start_time": "2017-11-18T00:16:53.094686Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.090419312520552"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "x,y,z,v,n = np.array(points).T\n",
    "\n",
    "# scale lossses to they look OK\n",
    "z=(z-z.min())*10000000\n",
    "logzmax = np.log(z.max())\n",
    "logzmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:17:32.836348Z",
     "start_time": "2017-11-18T00:17:32.829248Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(267, 266)"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now reshape into square arrays\n",
    "x = x.reshape((len(xs),len(ys)))\n",
    "y = y.reshape((len(xs),len(ys)))\n",
    "z = z.reshape((len(xs),len(ys)))\n",
    "# dz = dz.reshape((len(xs),len(ys)))\n",
    "v = v.reshape((len(xs),len(ys)))\n",
    "# dzv = dzv.reshape((len(xs),len(ys)))\n",
    "z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:16:47.455439Z",
     "start_time": "2017-11-18T00:16:46.760127Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/contour.py:1518: UserWarning: Log scale: values of z <= 0 have been masked\n",
      "  warnings.warn('Log scale: values of z <= 0 have been masked')\n"
     ]
    },
    {
     "data": {
      "image/png": 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VpPhYY9SBslaApi0EOyENGl7E1gp8tVHuvshSBkaPr9XQi0KrDpW83t/Zjh/T\nC0A4cfaB1gtpll/eh4Md2L2jVtDTYgM9TszZxrPCjr7v6Bi9py4a2g3YHx9nb4z5sIjcPnT3S4An\n7YwPROSdwKuBvxCRW0DBGHOqOuNqB3vQrHn1Oqzd6TZdeWM4Lb0NV+HI4M1SkagOkzjIKz3Tr6Qy\nkdEiWuUA4ukBD9peHMbNxwZtDczVggZq14f4grpOjpqRN+t64WiUoW4vJMVtDfiRFISiZzu1Kj6n\nF5z8ujbbJOcvl7dQB9lV2L4D+S39DGe2yP1gQUQ+1vP3E3aG9mm4DvReUdaAL7T/fz3w1n52Pgv2\noMH9sI/OOALE4spoE64yi9oZm9uClVv9vSYS02zyYE/phUGy+857NkM2hx2FegkOtjTIhxMQnRuv\nhr8zrzZmV0/NGtQOdAVRL+oqIZzUfftnNKDcj8DiI9p9WtzVC1J6+XKNSuztss2tw/Lty6tGOgmu\nD3N9c/a7xpjHx7l7Y8z39fvcS/TtGxHhMCyvQqMxvi5b14WlVc1sdzaHe/3coq4QDgZQDaUWlAMv\nDejZM86M3hjlrwvrmvllHoH0tck3a4UikFyChefA3A0N8vUiFO7B3md1hXEWQ9odFzKrarNQK2qW\nf9nUOl5Ii7Stpq4kZ5gU7gG9V5Qb9r6BcO6ZvYjcRIsRyygB+4Qx5sdEJAv8MnAbeAb4h8aYMeok\nj0A8riqd7a3xzbINR2B+SYu1uV3IDjg9K5FSk7P9XZu197FcjthpTcWcUjn9ZpQyJhrHGNi/p8XX\n6BwkFs8+6xPRC4sf0+NpVKCSg9IOlHYhmob4/OS5/URGaxH7G8rjpxbVYvmyIBLXlUxhV9/XZVq9\n9IOgAcWJ6+w/CjxfRB5Fg/xr0KLsQJiGT6YF/AtjzGPAFwHfKiKPAd8N/KYx5vnAb9q/J49UWqdd\nlYoqyxwHkmm9FXK63UGRtRTA3mb/gTi9oBlseYDsvhP4Rs18DzY10CdXNMs+7+W9iFoeZG7qCiOS\nguq+Zvrl3PCeRv0iEteWesfVoJ/fuFw69bkl/c4Uh+wen+E+ROQdwH8HXigiayLyemNMC/g24APA\np4B3GWM+Oei2zz2zN8ZsABv2/0UR+RRakHg18FL7tLcDHwK+60wOKpNVrfp+vjvXdlTML2mr+e7m\n4BOuXFetkLfvQWGvP3WOH9EgU8ypJLOfeoEbAkSVLsOiWlDaJL4wna6ZoTCEliGWgfKu3qr7EMtq\ntj+pC1OYFLkOAAAgAElEQVQorDx+Kac8/m5DaZ7LYCHtR7Q+1FlJelPUCzFpOD4kx6ezN8a89pj7\n3w+8f5RtT0Nmfx9WcvRi4CPAsr0QAGyiNM9Rr3mDiHxMRD62szPG6ToLizpjdm8XimPgIzsTrhxH\nh5YPmtlF46rOKeT6d7dM2Xm8/WZcIvrDbQw52avdUpokFNOh3tMMz9cawtxNvciVtmHvmQeHk08C\niaw6aQYt5fGHPdfThvSCfn/yA6w+ZzhTTE2wF5EE8J+Bf26MeSC6GtMRWj8MY8wTxpjHjTGPLy6O\nyeemg+UVbbTa2oTyGPziPQ8WVjRYD1OwzSyqBn6nz3GIflgzrvJ+/8XBUFQVLcNQG6Ud/aEnL9B0\nKz+q9E7aDrk52NCg35igpW8kAfO3lNbZW1OfnYsO14O5ZZ3PfJWKtUEDynf7u50zpiLYi0gIDfS/\nZIz5FXv3lois2sdXgTHaVPZ9YLByTWfZbq5DrTb6NmNxyCzo3NnigD9y14WFVVU/5Lb6e02nk7bQ\np9dOxxenOeB7bbeUvollLiY1EY5D9hFIrdoC8xoUNrR5aBLwQhrwwzG1WihsTb52MGnEUzrkpLB7\n+ZRHlwDnHuxFRIA3A58yxvzHnofeC7zO/v91wHvO+tiAbtOV541vtOFcVm2R97YHHzgSieqwk3JR\nRyKeBi+kjT6Vg/725UcBUSuDQVC39Ed00EauKUMkCfO3lcOvlzTLr0xIBOY4SunEM5rd59YufpCc\nW9Y+jdwlK0IfB8eH+M3+bud9qOd9AMCXAt8AfLmIfMLevhJ4I/ByEfkM8DL79/mgM8sWNOC3x6DT\nXlzVZfzmPWgN+AOfm9cMKr/dn596Mqv7KvSxOHKc4Xj7ehm88HQZlQ0LEUgswPyjmvGXdiB3xw52\nmcC+Uosa9Jt1lWde5GlYIV8nXNWr2nB10S9elwjToMb5Pe736T+ErzjLYzkRvq+UzvqaUjrXboym\n3HBdWLkOG3e1YLsy4PYWVmDjWVX3rJySNbiuDpHOb6or5mk2Cn4MSnsqp+tHh26MWhZc9Kz+MFxP\ni7i1ogb8/LMq20wsjl+f3xmDmLunAT97/eJaTceS3WJtfku98C8rggZUz5+P7wfTkNlfHESjsLQC\n1aoWbUeFH9ZxhLWqUjqDwAtBZkkzqEIfapsOn3qwdzo33Gn66XfyVWuCowwDa3rWqCitUi+pD06z\nZu2Qz4AqiCSVz49lbTfsM5NR7YTCqsd3PS3cjnOI/FkjmlBfoFp58E7uGSaCc8/sLxySSV2a7u7A\njqsdt6MgkVQuvZDTbtvkANlxIqU/pv1dDeSRUzLB1IIurUv5ky2QPV/pmHpZ+f7T0NHle2Mww2q3\noFHSpqxmtT/bYyekHiVeWNVEXmT8mbfjKrUTTkJxS1U79SIklsbrEuqFNODvramZWuaa9ktcRCQz\nUK+oO6YfuZwOoI4P0fPn4/vBLNgPg7mM8uz7eTssfEALhMPILmjA39vWbH8Qh8zOZKu9zdO978PR\nbvNLJHGy9UIkqd2l7dbpwazTceuM8HVq1qCyB02bzToh8OMawJ2QDd52kIkxehEI2hA0leNuN6Ba\nhk6pwQ3r6/24XgDGhVAYsrf03JT3oPGMNpDFBrSxPgmOC/M3ugE/e11VOxcRmRXYflYLtou3hhuq\nM8NYcLmCfaNBUCzijGPq1GlYWFQ6JJ9TpU56xB97r0PmtQFslh1Hu2u37qocc+EUP5+0ddLc39JZ\noschltKAVi32P3nJmCE89AOo7Kg1sbgQzUI4NZx80xho1bqWx9U8VHO63XBSb+MK/PGsXhCL29qQ\n1axCcozOlo5rM/w7yuNf1AzfdXvcMTeUvz9v+4xxwjSgNuPszwXBxgZBccJdkB0sLkE8ATvbo3fZ\nui4sX7MOmQPK1nrlmOVT3nunWNuoQfmEY/bsBKhKH3zr/WEjA/Ln7SYU7migj2Yh86hmycPq9EU0\nmMey2hmbfS4kV7Wjt1aAwl3IP6OGaONwvnRDMHddj7lehPyd8apPHEcDvudD/t7Fbb7yI7bhqqIF\n2xnOBZcrs/d9JBol2NBg6aQm7M0ios6Y62vqlBnytQFrWPhhWFjW7tr8LmQH6AhOZzVjz28rXXPS\nyiBuu2oLO5otHre0js2pcVetfHJWed8tM0BHZPaBdlODrwkgdWMyxV3H6Wb0QaC1gFoBKrtKGYWT\nEM2MXmuIZ9VaubCuAT99XemesbwHV1U6+XWVzjrexczw4yn9zA/2tC4xzNjMaYT4ELkYnP2ly+yd\n69eRWIxgc5Ng/wxUAJ0u207TVX1ELXYipWMMC/n+mqZ6j2N+RYNaP921neaX/RNUQJGEBpfyKU1F\nncy+3w5QY+DAzgxI35y8xz1o4I+kNOOfuw2RtGbj+8+q131zRI8aP9YdYrF/d7xqHREdcej5GvQv\nqp9Oal7pwYO9mUPmOeDSBXtxHA34iQTB9jbB3t7kd+q6Xd39+poOQBkF2UXtsN3d0uJrvwj56p9T\nLZ8+7MQP64+vWoTKMYFJRLs7G5WTA0ynMBv0SWFU89CuQ2J5PAqeQeH5qqLJPgdiC8rzF+7aoD+C\nJUYoDJlb+p4ONnR4y7ikoY6jRVsvpAH/ojZeZVesG2v+4ttDgHL2jbv93c4Zly7YA4gIzuoqkkoR\n7O0RjNMN8ziEQhrwQTP8Qbtie3HfIdOF7Y3BOnaTcxBLqBzzNHuEREb51P3t4/cRn9NgXjzhoumF\nUWvkPgJl0FYKxbejAs8Tjqv8fuZRiC/aoH9HA/WwnjiupxOyohm9qOXH2EXquJCxDUq5e2czcWsS\nSGbt9+AKGaZNAS5lsAcN+O7KCs7cHEE+T3tzDE1Qp8H31Vah3R7dVqFTsG23Bi/YZpc1MOye8joR\nlcadROeIqC1vo3J8k4/jaLbcj1Nk/QAwGmSnBY6jwTnzqBaKGyVbyN0bLjMX0XGEqVWVhObHaLXg\nhTTgt1uwf0HthMPRboNfP3Yf0wzxwb/Z323cuxZxROSHROQnROR1pz3/0gb7DpylJZz5eczBAe3N\nTcykfxyRiHL4jYYG/FGWquGIFmyrFS3Y9gvXVTuFZkMz/JMQ8jXTqhaPD+axtCpPTqIlwgnlvU/L\nNhvlbgPUtMFxVFmTeRT8hAb7/DPD2x1Hkg/y+OOyTfYj6qdTL8PB2ZvBjgWZFf13b/18j2PKICJv\nEZFtEfnzQ/e/QkQ+LSJPikhnat+r0Xm0TWDttG1f+mAP4MzP3w/4wVkE/FhMh5fXaqMPL+8t2FYG\naJ+PxJTSOcirHcNJSGZt8W/r6NVIx6yr1The/udbSqZeOnlfrZpKIUdF0NYaQdCyKqAxwvU0K09Z\nWu5gDUpDWhB3eHzH07m84wr48TlVAVUKcHAGNOW44YVUAtys64zlC4sGtO/2d+sPbwNe0XuHiLjA\nm4BXAo8Br7WjW18I/IEx5juAbzltw5dLenkCnPl5cByCnR0CwFlZQSbZ3JFI6PCTrc3Rh5dnFzVg\n72zAtUe0PtAP5ha0WLu7od21x0ksRSC7Cjt3lM6ZP+JYI4muQVo0+bAdQSisGXu1cLwhWtDWwOwO\nqKM3gXbWNst6sQiaPKzpl65tghsGz9omyAj5jB+DzG3N8Ks5XZUkVwdvzHI9HZCyv6YBP31N3TRH\nRWoBMKqWclyl2y4SYklV5RRz6qVz+bEgIh/r+fsJY8wTvU8wxnzYTuzrxUuAJ40xTwOIyDvRrP4u\n0KnUn8oZX5lgD+BktBM02NkhcBzc5SMnHY4PyZRmyqP66IjA0jVYf1YD/urN/roQHQcWr8HmHZ1u\ntXyCs6Yf1gz/YA9qqaO13KlF2H1Wi7XpI95LNK3ukK3G0Y1RHYqn3wBsAqjl9UagnbBeVFcR4na3\nYwKb5TeVI292MkXR54fiEEoMZ78sotSOn4DSpqp2olmIzQ/WCeq4WrjdX1M9fnp1PAXq1KId9r2r\n1NhF0uCL2FGbu9rkdyG9c3xw++bjd40xjw+xk+toYO9gDfhC4MeAnxCRLwM+fNpGrlSwBxvw222C\nXI5ABGdpwiP0en10PG/44eWhkDpk7mwM1nDlh1V/v7uh/H3mhNcls7qkzm3C8u2HVwKhsDZaVfZ1\nmPjhH2c4qcG+WtAC5Sho1aG8rgE8lIBwpv+M2gTQquqtWYbqjt68KPhpVQINuqoLRSB9S60dqjlr\nj7A6mAma4yqH3wn4yZXxDGVPLysdUtiE0CPjNWabNOJzKsPMbcLyI5fLSmHCMMZUgNf3+/wL9K0Y\nH5yFBTCGIK9GZs7CiEZmp2FhUQP+3q4G0NSQvu+JpLU0zisnH+szi4vb1x3krRnaMRllR52zc0e9\nyI/yIU8uQK2kY/QWbj3443Q9Dfi1AsTnH/aJ6c3ET0KrDsW7+vzEzcFpE3FsNh+H6ILKKBtFaBxA\nZRMq2xBOQyQzmHmb49jegCiUt7UhK3VtsONzHM3wC+tQtAqxUQO+iHrn7D6rHc/ZEWctnCUcBzLL\nWqitHJw+a2Hq0CAwE9fQ3wN6lw837H0D4UoUaI+Cs7iIpNOa4Z9F49XyCsTjaqswio9OdlGz9d3N\nwUYkZuzrclsn9wD44a5Z2lE+5I6jmWSrDqUjuiBjWQ3m1SNe63qAnNx8ZYxm9OJAcohAfxTckFIv\n6dv24hGHeh4Kn4Xy1uCa+khKs3zHhcKaXtwGgeOop04opgG/Oga9uRfSz6VR1QvxRUI0oavG8gX1\n/pk8Pgo8X0QeFREfeA06tnUgXNlgDyrLlHRaG69yE27f7tgqxGJasC0POZii03BljAb8gV53zb7u\nFIVQYk6538LO0Y1Zkbhmo6Xcw/rxkLUWruSP3ofrd/3vj0Itr9RNfGUyIw5DUUisQupRze4bB3Dw\njNIzgzQpeb4G/FBMlTrlARUxIlqovR/wxxDooklIzOvF46IF/FhKefvGCB3M5wIfR272desHIvIO\n4L8DLxSRNRF5vTGmBXwb8AHgU8C7jDGfHPRIpyLYH6UtFZGsiHxQRD5j/+3TZ3eg/eIuL2un7e7u\n2QX8SEQlmbUhv9i+D/NLqtDZH+CYvZDy9/Ua5E8JTpkVzVxzG0dLDlN2NN9RjT2xrPrNH5XdhyIn\nd9rW98GLj0eeeRLcEMSWIP0o+CnN9A+egdoAfkqOo6ZnnW7ZgwFltp0M34/rQJR+HEZPQ3K+K8k8\nqet52hBP68qvMEA/ySWEMea1xphVY0zIGHPDGPNme//7jTEvMMY81xjzQ8NseyqCPUdoS4HvBn7T\nGPN84Dft3xOBs7yMJJMa8CdN6TiOdtl2jNOG9dFJpJSLz+9q01W/iCUglYHi/sl2yK6rAb/VOLq7\n1nFVkXMUneNHNYCVj7AS9qJ6ITjKg6ZVA9NSP/t+EbRVgdOqqd9Ov/4899+HB/FlSD6iks3qNhzc\nOXn1cRjxRYgvaedtYW2wFUInw/fj6os/DkontWBXXntqK3wR4Dga8OsVbQa8MGjQZq2v23ljKoK9\nMebDwOEU9dXA2+3/3w589aT2LyK4vV46kw74HeM0GM1HZ2FZO2B3NgbbxtyCFmpzWye3q0diapZW\nOTiaT40kukHl8PI7vqBBvXz4QmCLyo0jGmnaNsC6p0jwWlWobkHxaSg+BaVnoHwHSs/qfQef0f9X\nt5Sm6Sf4emFI3oD4qtJIxTuquuk3U4/OqTqnVdOAP4gfzkOUzjgC/pJSTfubF8dDJ562dZAL2CR2\nATAVwf4YLBtjNuz/N4EjRfEi8gYR+ZiIfGxnRMMzd2WlG/Dzp7hGjoqOcVq7rU6Zw/joOI7q742B\n7QEoBJHuRKvT/HNS8zoSb3/7aP4+taSUyP4huicUVhvh6v6DDo2Oq0GtfsSq4rTxhkEbyvegfBea\nRXCjEFmE2CrErkPsGkSXwZ8D8VRvX93UC0L5rlJEpwU+Pwmp2yr3rO2qKqjfAm44CanrerE4GCLg\n9xZtT+tEPg2Oo7bIQRvyG6c/fxrgejq3tlY+3ol16uDjcqOv23ljmoP9fRj1NzgyIhljnjDGPG6M\neXxxcURtNzbgJ5PaeHUwYVe+cFgpnWZz+IDv++qDU6/pDNt+4Xld/v40/5ysdeDMrT/M33fUOe2m\neuf0IrGgQax46LjCKQ2ID1kHnHDRCVqavbcrEFmA5HM1yIczEEp2G6f8tF4A4tch9VxIPALheQ16\ntW0oPa0Zf/sEqsBxtYgbt0ZmB892Z+OeBj9mA35LG7CGyfC9CBQ2RjdPC4WVamtULo6lQq8T62Ww\nQJ4iTHOw3xKRVQD775k5PjkrK90BKJMecRiNasAfxTgtntBJVcWC3vpFLNH1z6mckEm6rgb8dkv1\n94cRjnWLgrWe7Tiu0jnNyoPDPMJJ7YA9XAwV28RljrjoVTf1/thNCGf715G7YYjMQ/K2Bv5QCpoH\nSv1UNk4O+n4SUo/oyqV0D6p90nuhqPrqmPbgAd9xNOA7rvrrj2qPHEtrI1w5r5Ouph0iKv0N2trN\nPeUwNGmy3tftvDHNwf69QMe283XAe85qxyKiA1CiUQ34pQkbNY3DOC27oANP9rYHK/pmFtVdc2/z\nZP4+HNXCX7V09JSh5LwqbfYPbSc2p5lqcbvHLkGU4mmUHqR4OlLLw7RJq6K38IJua1i4YaV5Es/R\nC0arrEG/unU8veOGIHlLFTu1PSj1+fmEIt2AfzBg0db1NOAHbQ34o3Lu6SUdQlPZh9KE6clxIBxV\nGWmlcDEtnKcUUxHsj9KWAm8EXi4inwFeZv8+y2PSgB8OE2xsYIbVxfeLjnFapaI6/GG+5Asrmhlu\nH0G3HIde/n7nlNclM/ojLOxC7RAF0xmdB8rf9x5/0o5ALPVQCdEMIFoE7cC1tsftQ8XexoFm/f4x\n3ZWmDa0SNPJQ34XGHjT39QJx1CrBcZUKSjyq/H4n028csyoSUd1/dFHrAMW7/QXgUEQpnXYTDgZc\ntYUiyuG3GnAwhlkMqUUtqBd3L4aWPZ7W8zXlaiIhRIhrfd3OG1MR7I/Slhpj9owxX2GMeb4x5mXG\nmDMfWimOg3PjBhIO015fxwyri+8XyZRaK5SKsDPEktvztOGq2RiQvw9pwG/UNcM/CZll5YJzGw9T\nDJ0uzmbtQY13KAyxDNQO1IMdNOBG53SYSSe7dzxw/If58XZNJZuHqZugBbVNKD8NtXVo7EAzp8G+\nvg21NSg/BZVn9SLQPsSBOy5ElyD+iHXs3ILy2vHyzUgG4td0O/0WbkNRtVRo1aE44KrNj2ndo1F+\nWNU0DNJ2qM1F8MAPWSO9+gWdtzuFmIpgP824P9PW8zTgjzJusB/MZdQs7aCgbpmDIhqDuXkdVj7I\nwPJoXCmdSunkgq3jKH+PUT+Tw8ErmlSeuJx7cBhKfF4DarGHMolm1Rah0rO/UFyllb2Zs2l3+fwO\nggZU70CrCKE0RG9C/HmQeIHeYs+ByA3wF/S1zRxUn4XKHc3ke4/b9SF+EyJL0K6qbLN5DHXnJyBx\nQy8Ixbsnc/73XxNXT51mpeuH0y9iGbVnKO+OLsl0XPU2atagPIYGrknC9bRTu3Iw1VSOoUmdzb5u\n541ZsO8D4ro4165BEBCsrWFGGTfYD+YXID2nTpm5IYpUc1mIRHVg+WlzaHuRykAiDYXcyQXbkK8B\nv1HTgScPbWdJdeuFzW72L6IDQYJ2V53juN0xgB1ljp8CjBqXdXA4ozdGM3mA2CMQXlIZZq91suOB\nFwM/C9EbEH+uPo8A6ptQfUaDfi/Cc5rlOx5U1lV6eeT7j6pvDwaKa/0F/EhKh5s3ihq4B0Fy2Uoy\nt04e/N4PYin10j/YGd+oxEkhntbvz4UebjI9mAX7PiHhMO7165hmUwP+pGVhi0vqjpnbg/yAS/je\ngeVb64NJOrNL3YLtSReKSLzbcFU8VPQTgYz178n3ZP+hsKp26sWuOiea0aEjZTvy0Asrd9/Ln4tn\nB5ZYtEua2YeXlPbpRdDQx1tFaJf1b2M0uw/NQew2RK4Drgb9yrMP1ghcH+K3tD5Qz6mu/yiXTi98\nKOD3QenEsrb3IKeUVr8QUf97x1O3zFEVOnO2tnO4N2La0DFI298a39D2MUMIEWalr9t5YxbsB4BE\no7jXrmHqdYL19cmPN1xaVh5/bxcKAy67PQ+WrFxydwBTLBFYuKZZ8vYpQ9NT8/qDLOw8XLD1fA0q\nzdqDGu9Y1qpzrNukiNoNtOtdKWY4o393uHvHf5Bvb5X0AuD1WDW3S1B7Rm/1dWhsQP2eve8p/X/r\nQAO3F4fYLYisWg+fO8rxdz5PEVXtRJdVsVO+ezSP7/pK6WCgdALX34v4Utc87SjLiOPguFqwxcDB\niE1SjqvF9FZj+vn7eds0eJRlxwwDYRbsB4TE4zgrK5hKhWDzDHi4pWXV0e9sD26NHIlCZkEpmYMB\nLhaeB0vXlXLZuXcyZ5pZ0cCe23hYuhlJdCV/FXvsnSwVVGVijE5sCsV1/F+7aYeLeFCzKxovZhU3\nNjia1oMZfWtfAzwC/hJEbkHktv7rr4Cb0gy/sQm1p7WQa1rgJTXTD82peqf67IMXFT8N8Ru6qijf\nfXB1cf9chSFhz1WxD4mliNoqOJ4WbAfJWD0fEks6OKU0omFYONZ1yDxurvA0wAt1h+qcJA0+JwS0\nqLLd1+28MQv2Q8BJpXAWFjDFIsH2hD9EEZ1f27FGHlTzn85o89Te9mCGaX5YFTr1GuydsDJwnO6Q\nk90j5IXJBTVGK2x1OWI31A1aFRvQE3ZiWGnb6vCz3UlTnvXTafWMG+x025oWNHfAjUP4Fnhz4PTM\nnnXCEJqH6KMQvgluAlp5qH0WmjZghpe0mGsCzfJ7uXwvBjH7WPmYgqwXgcQ1e1HoQ3HjuKrQCdpQ\nPMWu4jCiKaWCKrmusmlYJOeVvy+MoRYwScRS+p244o6Yo2IW7IeEk83iZLME+/sEI3rynIpea+Tt\nzcGtkRdXuoZpgww8iSXUNK18oEXb4+CFtGDbajzcYduZouS4yt/fV+KkrMpkT4uzbkgLmM2yDgMJ\np5XLr+5q4PbiXRWNuBrkQbl5YyC0pPtql6DxDDSehuYdaD4Ljaf0PlMCLwORR8FNqkKn9oxy+15M\ni71uRLn8ek9g8SKa4RsDlbWjM/xQTLX4rSpU+qDNvLAqdFrVB9VI/SBpC+AHG6M3XM2tWm+jIXs7\nzgL3s/vi1DliOnhEWerrNm6IyEtF5HdF5GdE5KWnH+sMQ8NZWMCZmyPI5wl2J5x1OI4GfNcd3Bq5\n1zBtZ8BMMp1VK+X93ZMVOpEYzC3pcvtwBua4GvDbrQdNuRJLynsXrGY/OqdBs7yj/HfUcvn1gjY/\nBS01QHMiGnCDFpiGBn8nBO0itCyd4y1B6Dp418BdAAlBe1+Df2sTvJRm+uIon9/YBhzN8ENpeyHo\nCYBuuBvwj9Pi+0lt1mocPNgsduw5s1l6Na9a+n7RUTYZo+duFPR6G02zHDM+p+/7AlgojIKjZnvY\n+18hIp8WkSdFpGP3boASEIHTPZRnwX5E3J92lctN3inT89QpU0SN0wbR/A9rmAZqmBaOqENm/YRV\nRWJO5XLFnK4GHth/pMeUy14MHMcGraDL3yesuWlxUzXtXkyzezesPH0916V12r20DtDeBYlA6JY+\n1xTA7AFF3ZeXBTcLNKG5BkEe/Oua7bf2oX4XaEN4Gfx5LejWDwX82HWtH1SOUelEs2rMVtvtzzwt\nvqTbLW4Ozt8nl1S7P2rDVTimdE5pb3rtkF1XBQHV4sNigHNEQIsKO33d+sTbODTbQ0Rc4E3AK4HH\ngNeKyGPA7xpjXgl8F/ADp214FuzHALcz/GRnh6Aw4WJXKKTGaUEwuHFar2HaIAVbEVi8ro0uO+sn\nX2Tmlqwl8tbD3Y+xdLfhqmqll6Gw6sibFaV03JAGwJbl82NLgNGxgZF5LbS2arbT9kAzdtOyEsum\nFmPNPrTXwNT0cfH0/2YPyIMTBScFQVkzfTcC/iqYOtTu6Lb8efAXlSaq91BTXkStlIOGavGPWiXF\nVzSAlzZOl2R2CrYYKA1Y8I+m1VSuvDe6Zj61qO9lmt0xExldJV7cWbULHTt2e3vD4SccM9vjJcCT\nxpinjTEN4J3Aq425n23kgfBpO58F+zHBWVlB4nGCra3JG6eFw0rpNBqDG6dlF5SLz+0MVrB1XVXo\nmEAVOsddZESUv3c97bA9rKBILWnBdn+z69HyQNGxpPSGn1QuO2hrkG8WddHqRpRP91IQ1LquyJ0s\n3wgEeyBxjLNI4DQJpELgtgkcFyNgzAFI0Uo3Q9DaAKrg2zmh9bsQ1MHP9AT8Hh7ei0FkWb13akcE\nRxG1VQAo90Gbeb7KT5sVpXQGQXLJWiAMSM8deQwZVefUJuwDNSxEVOpbK01Nf4CDR4zFvm7AbseO\n3d6e6HM314G7PX+vAddF5O+LyM8CvwD85OnHOsNYICI4q6tIJKLGadUJqxtiMZVlViqwPeBw6cUV\nLXrtbAzG/Yd8HVreqJ889MR1Yd5qwvcOmasdLth2qIvkkmbNB5ta6E0sKw9f3NCuWjcClW3tiA1a\n+jpxNeCKC4E936YMBBh8DBsY2hjxMLQ06EsD4xgMLUxwAE4ATlz5/GAP/BuA82DAD2WhWVDPnQ78\nlPYDNPaPNlBzQ5rht2tKQ52GSFppq/LuYCMRHRdSK6oSGlWOmZzX4y7uTG+xNpaydZMLm92PDcaY\nXzHGfLMx5muNMR867fmzYD9G3PfRCYXUR2fY+bL9IpmC7Lzq7wcxTnMcWLZyya1TGqcOIxKD+WWo\nliF3wj5DPmSvKb2QO3RhcFzIWm16p8O2M7gD0S5RUHojaGsDUnxFVxX1gurfmwVwYhBUAM/+69pg\nD4YCRhwMLoG0aEuLthja0iJwygROA+O0MFRBquAklNZpb4F/DQ34VnkTXtCVRGNPs/z752JRs/za\n9sMma6DBOzynw8z74e8Ty1owLg6ojPFjWtyu5keTY4royqvVmN5h5eGo0oQHu8MN+xkzAlqU2Ovr\nNqTMFgkAACAASURBVALuATd7/r5h7xsIs2A/Zojr4ly/DiK019Ywg0gdh0F2Xs3TCvuD+eiEQhrw\n263BRhqC+ueks1Aq6OCT4xCJqUtmrfzwXNFQWC0VmjWldKDr495uKi0Rilg9flktFqKL2tFqXMDR\nDFg8fb4xmt0GbeXuqWKoY6RBQECAT4s2LanSlDotadB2ygSOwVADKWvAN1UIdrRwC9C4pwXZ8LL6\n79Q2Hwzs0VVdWVQ2jinYLip/Xz7BM78Dx9WA364PTuckFq0cc7P/MYpHIRJXWq2cn8omJkALtcZM\nd2/AePFR4Pki8qiI+MBr0HkfA2EW7CcACYVwr2sRNbh3b/LGaQuLmuXn9tQts1+EI6rQqVUHs1QA\n1d/Hk5DfgfIJ07ziaS2slfYf9tCJJLTpqlbsZpJ+VCmdRllpiUhaxxhW9pTWCSU0Uw6lbVHWAYIu\nVWTaEDSRoAmmSmAqBLRoUacF1GhTo0FDyrRoEThFAsEG/JLN8CsQ5DTDN021XhCBsA3s9Z7A7rga\n8IPG8fx9bMUqePpYfYUTtl6x9+Bgl9Nwf2Vk4GBrNBomacdJFgb8TpwV/Iie96OG6JwxHDwSzPd1\n6wdHzfYwxrSAbwM+AHwKeJcx5pODH+sME8EDxmn37k3eOG1pGeJxpXMqAxReE0m1VCgdQG5Aznd+\nRZfVe5t6wTgOc4tdD53DDoaJrGaSpb2uQiea7hZsqwea7bphy99n1GqgfqAF1nYDpW/QABzUITBg\nAiRoYCjRpkCLGnUCSjQ4oEKZMnWp0KRJ2ykSiGBodDP8oKhZfmhJ594293S/4RVrvdAT2L2oTr5q\nFI6ma7ywdgQ3i+rweRoSS0rnlAYMtm5IM/xmpduZPAxcT9U5jUr3M5kmdEYXNmqXzhHzqNke9v73\nG2NeYIx5rjHmh4bZ9izYTxASjeKsrmJqtckbp4noaEPfV0nmIF22c1lIWmvjQWbYimjB1gvB7vrJ\n3Y3ZVc3IchsPT0pKL/codOxFI7nUtfVt1tReAFE6JLqsmXK7ZZul2vpYuw0mAoFB2g0wbRxTB1Oi\nyQE1SpRw2aHBNiVKFKlJzWb4JQ34pglSA4mpbt/xla9v7qnXvRfrFmxbPYE9PK8XpNoxdE0kq49X\nto+me3rhuKrOaVW1m3gQRNPdzuRBjNYOI5a2Vs9T2mgVT+l5Oucxi23aHJDv63bemAX7CcNJJHCW\nl9U4bWvCy2LHUQ2+56kkc5B6wfySDjDZ3YLKAEW+jiQTTnbJFFGFjuuph04vH9xR6LieFmxbza5h\nmhvqFmxTls+v5mx3bQ3E1yw4EJCwZvZGwIRw2gYnaOPQRChSZ48C+8AqNebYokaFMg1p0MIQOGWM\nC8Y09JchIdttaztwG7Zw6s+r5059qxu4RVSOGbQetFrofY+xZe0J6Eudk9IVQ3l38EanxJK1Qx7R\nTiE5rxffaczuQbP7evVhenCGIzH1wf6YNuELBSedxpmfxxwcTN5WwfMebLrqt14gopbI4YgWbE/q\nlH1onyFtumq3Ttbg35dkogG/99g6Ch2AvB2y7biWhwb271lLYds12mqo0qVVVvsE0P1KRAN/u40E\nLk7bwWsH+KZFmAqQo0CeLVKsI+Qth9+SJm1cAlSaCRWVZJqm7bRd1v+39ix/bwN345CHTjijdE7r\niPPnRaw6Z78/eWViyXL9Ayo5HMcOLG9CZYRAGEvbou/2dPrJx1NaUD7YPbdisotLikxft/PGVAf7\nE9qELxyc+fmurcKku2x9XwN+szlY01VHkul6KskcRDoajnRdMk/S4Id89ShvNyF36Ng8XzP8VrMr\nyfT8rkKnsK7F2mjGet/baVTNqg34rt2eC4ShBdJycQMP3zgkjZClToPPUuAuMEdAjCYubYwqdySw\nkk2AknbatvNamPVS6pgZNFT337FG7lXnhOc1q64dU4yNzOu2qn0Ua70wROb0vQ6ivQdVMoUTUC2M\n1oB01BCaaUKmY69x/sXaacepwV5EPigin3sWB3MEjmwTPqdjGRnu8vL9LltTnnCXYjQKSytQrQ4W\n8F13eA1+LNGfBj8cVR/8elU5/F74UQ0wjapOUurcl1rRoH6wqXx2p8M2lNSMv9VQfj1wVKETCBBF\nAgenESLU8okYnwUT43l4vIASy9SJk8YjjiFMQAhDCCN1jCNASwM3DrT3ILQISNca2beBu7dYK47q\n8ts1NUQ7DMeF6ILy8f0Ua2N2H+UhbAxi87oyKI+g8fb87hD5aTRKcz1ttKocnEtXbZs2Bfb7up03\n+snsvwv4URF5q4isTvqADuHINuEzPoaxotNl215fx9QnPAM0mVSVTrk8WJet73c1+FsD+u/0avD3\nTwgysaRyrtXSw1OIIglt7qmVoGAfiyRVaVIv6gzb5Ipy2qVtVeiIA622NUBzrGcOQBwJQjjNMH4r\nQtxEWTBxrhNmiYAodcIkcUki+IgYwAMMhhCYA3AzEJTswJOMHYtYt6MOs6rWafUooPyUFmPre0df\nZP2UHmd1tz/v+9i8UleDOGOC9jJE0lDdH017H03qKqOyPzU2BQ8gmtTzWJ8eg7RpxKnB3hjzx8aY\nvw28D/h1Efk+EYlO/tD6g4i8oWMstDNpX/kxQBxHh5e7rip0Jq3BT6W7XbZ7A9QLwhG1Ra7XBm+6\nmltQPrWwp0H/OCQzPRr8Q8vw+JzOq63sdzX4sYxSONV95aJT1zSrL2+rtbCgskvHt5l9GAJAEkg7\njNOIEmpGiLejpIxPGocoBoc6LoJDBIhg8HR/EkKzex8QCPZ1OIo4SueAUjniqSXyA+dvQTnzo6wU\nRCC2aCWcfUwfi1hv/0EHlQPErb57VFVNesnaIE9hMTQc1YviOXj6uLikmevrdt7oi7MXEQE+Dfw0\n8E+Bz4jIN0zywCxObRM2xjzRMRZaXFw8g0MaHeJ5Osu21TobDX52XoN+PjfYLNtYHBaW1TBt0Kar\n+WVV9+xtneyDP7eoy/DC7sN+J6mFrga/MzovuWilhbtqmpa63qU5Igu2kxZVyxiAiAZ84ogJ47Ti\nuO0Y4XaUaOASNi1CNHBoIQgOHo5EAVGlDwB1cJLWbM1RZ812USkSEQhlNLvvHVweiiuv38gdfaHs\nPF49JvvvhQjEF6y3/4DKGNdT7r42InfvR9UGuVKYPu5eRGW91dJUWChMK/rh7H8fDbA/glIo3wS8\nFHiJiPTr2jYsxtImPI2QSEQ1+PU6wdra5AP+4lLPLNsBAkYy3dN0NcDKSUQLth0f/JOarjLL6neS\n33q4SSa93B2d15EAJpfBj6sGv1GBtPX4L++qXw2dgB9RGSZhpXZMFGmHcJsxQs0ooXYYP/DwTAvH\ntOyPwYBJ6r/iAb4OSHESQFutkr20vah0bJrTmu03D11Iw/PdgStHITpvFT19ZPfhpNXp93FxOIxY\nRiWi1RGz+5gdIDONM2tTC9ZH6WxXHm3afarsB5wfPQH0k9m/AbhujHm5MeZfG2PeZ4x50hjzT4Ev\nm+TBjatNeFrhJBJn23TVmWW7vTlYl+1cFlJzUMjrrV84jkoyvZBKMhvH1ChEVKHTabrq9cHvaPA7\nTVe1ctcaIBRT07BWHVI39PnVvZ6Ab2yG74DxwITARAEfacdwmj5eO4TXdnGDNmKqiAFhF+WEkj3v\nxco7TV236fhdW2VxdHh5q/hgw1Qors9rHHPOOtl9rU8lSWxeO4YHze47ypxKbjTdfSSupmvFIbT/\nk4Yf1hViKT91owunBf1w9p80x0ehrxrz8Ry1/5HbhKcZTiKBs7KiTVf37p1BwL+mBdjN9cG6bOeX\n1Asnt6NZfr9wXVi6oQFx+97xemjHsU1XIdg7dGEQgcx1VYbk11Wp0wn4XsSONWxA+qZmvZVdbboC\nG/AjQEgN1CQMbReMjwQRpBXCaXm4bQenHVhPnQjCdcTUgIZq98Xy+J3Zt05cbZU7n5eXAkzPUHQL\nf07pl6N09wCRjOX2+1DmhBOa3fcz8vAwYvN6IRp1qlV6Sbczja6Y6UXr6XN2tTsXt0+VferMjuk4\njKSzN8Y8Pa4DucpwUqluwN/YmGzA73TZDjPLdnEForHBu2w9D5ZvAAa21o7nVV0XFu2FYXftwQzN\ncWD+hq4ScvfUcsFxYO6GKkUKdipU+uaDlI44uj8nrBy8LdgSYK0VXCQII4HB+f/bO/MY2dLyvD/v\nObXvVV1d3dXLvXcmjIixY9nWFdhyFKEYwhgjxkucgC3F2MgIyySOEskBEZkkDpIdS5FNsI1GAWEi\nzJg4Jp7gsVkcE/4xBuKFDAyYYWbu7a7q2rqql6rq7tq+/PGcc6u6b3X3ObV39/eTjm53VfWpr5f7\nnvc83/s+b1dg9ARGrwnpbQO9HQA+QBIDGbv1X8YIMNAra41m0HLgPPNz8cW4nvY5F0hflJuvJw7v\nmEIpK7t36Qnj9XOv42hvvAYpj4/NVs39xXPFNE0OJj9uPDwlTbPYTVU3CSMWg7G8DFWvo1dyOSPW\nLaPOshVhhY7X577L1uvrd9mWti/osvUAaUuSqWyfDiiGCaQ2+G91e0jAz/czfDE4ytC/ZA0j7wwE\n/B698JUAys+Mv2sCPeu5ngkgChhrgHnbumBYmaxhFaI9yPQHLlxmiBu1p35mBg3b2ofna+2+OOvu\nuw4uvL4Ivx+3FsgAN3mB8UzSACBiVfgsYmVOJMm/0xkZpHXQc+hmP3/DNh3sFwgjmaStwv7+9G0V\nBmfZ5i/Ith9apAGsbozeZZtZZ8Zeyl3cZZveYDZd2T6diZoeYGnTmnSV43CUswG/dcSAb3hZlulL\nUO7pnFCSeVCa6ePRA1i102XpZq/Dzdd2EWjfB1ovsIvWiPWDPYas3Qw+sFg+/f1ELeO2c7JNfxyA\ncDDLZYhwUEnnyL3Rmelhv8LxwXgVNaaHte3N/cXT7kW4v3M8/+C6aOhgv2DM1FbB7+/bKuxcEHzP\nYpoM+BCgsO3OcC0QojXyyRGHl5+b7foZ8HtdoHzmYmR6mOFDmOEPBnx707bVYJWOJ8CAb4YpqXSP\nAXiZ5ffEqtYJWLbIprWRawDwM/sXLx0wPauAd7W/hp4VaA1f/zHxWs+d+Xl4QgxCnXM2xQ2Tm7VO\nqnIAXhzEsCwjXOKP8iJ6PKa5WSQFQC2mdh+O846wOX0DNw8Mh272kam8v4iErT6jN1z2Wh3sFxAj\nk5mtrcJKlpu1bgK+1wusWgPI3doqhKN9W4Xdwvmv8wW4adttWxn+wHt4vMzwxQB2twYknfV+WebR\nPgO+ba2ghLX43ROWZnpsKUcAeEFvHevz7ok1Fctg2aUR5vsqxSqczi4vILacA7DeHwBPPoAYrLo5\nL9gDXKPqslP2MgyDQfvk0H1m7Q/zAjiulGMPKG/uPWxZPW9CUa7vCvrliMiHRKQkIs+eefw8Q8h/\nA+DjTs6tg/0C8mB4ud9PW4VpDy+PRICVVZZjFnYuf72Nz09bhU6bGb5bW4XkMqdc7V7QsOUPMuB3\nWsMD/ikNf6BKx268qpeBWBYIpthY1GoCoSyAHu8IPFEABjN72J2yCpAgJZtuHTjJA0ffAo6+CRw/\nz8/FSyfMU9gXSnn4+zADlv3yedJVhF/Xdig/BBJ8P7dlmAA977ut8fzuAWr3hslSzEUjtsTf75QH\nk3fQQxlNR4dDPgzg8cEHzjOEFJHXAvgaAEebfDrYLyhiGDA2NvrDy51uoo5KNMbxho06ULwg2z5L\nIMhN23aLGb4bLTiWBOJLtFS4yDgtEGIdfqfFssxhGb7pAXa36Y8iQuM021phP8/Sw4hlptaoMOCb\nPqB1yKBuhhgAlYeVNt0j6uJGnOMJvcuAmaA3jn8N8N8+ndUDDOZAX84ZxPBb1Tvn/B5FLAdPh3dy\nHr/lvzNCs44/am1gj7nBahi0tFjEiVahKO8M9xdjMLlF2rZ2sY63nX2BUurzAM7ekpxnCPlqAN8L\n4CcA/KyIXBjPPRc9qZkvYpow1tbQvXcPvUIB5sbGdN8wkWR2XrU82zNnM9dzCIU5y7a8AxTzwMoa\nv97Re1r13wc1S4ZJD39dIMyAv5tnwF+yykeB/qbt7jbLMpNZa77tMp+rl4HaFiWe+AZwkAcO80B4\nhdr7SY2B05fkBKpemxm/KKBdY2esJ0ofHDMwfH1K8XWGl+c6i2H9V+t1+JpheMOUtrpt9htcRiBG\nm4hOi7KFUwyjb5Bmzw0YlUiSoyMPK/yZO/29z4LEClC+D+wV+bczBTwwsIyQ05dXlFJ3R3ibYYaQ\nr1JKvQMAROQt1rkvvLXWmf2CIz4fjEyGNfjVGWiQqSUgmeLg8oqL5pRI1PLRaQBlF3cGAOWciDUW\n8SKnTDvgt08elnQMk3X4vgAbr+yW/lCSsk7nBKjep66euM2svr5DfT68xiB8bBmd2aWSnRYzem+M\ncs7RfaD5InBSYrds99jS9g/5XO8E8GXOWbz1X+2i/48eq9Kn41C281kdvk7vBgYJWJYQ427UApxX\n220vngWy3VV7zd0wlVIfVkp98rLX6cz+CmDE4wz2u7uQcBjiH5I5TpKlNDP8vZplZXBOtn2WaJwB\nuFYBKgaDv+P3XGF2vG/dVcRTw183mOFXtlmxY2f4hslO270deun0uqwa8UeA5CblnNp9avjxTVbp\nHFWBTggIrwPHu8BJlXJKcJUfH5cBTxjwr1uTqg6Z/Z/1wREPEFjja4dijy+8IL/y+MFZug6tr00P\n7xJaTUpWbvAG+H4ndXrejIM/xHLHRtWaXbtAOaRh8m+506bkN2E66KGEqTdwXWoI6YQF+q1oLsLI\nZADTRDeXm75+D9A4zXbKrLoor0ukGKgP990ZpwGUgsJRYK9CWec8AuH+pm1563QdvmHQSycYo7Sw\nX+RFxBsAkresmbY5ShiRlb6Of5DjSMHQCq0NGiXKNoFl6veNLVbK+JaB8MuA4G0gsM4AH7wFhB7h\nHcF52A1TxiX5lel3HuwBVh61m6PVzXutKV+TqJWPZXieRTNJiyRmbqEwBSZiCKmD/RVBTBPm+jrQ\n7U7fNM0ms8KAX91154WfSveN02ou67CXrIBfK18S8ENWHX6HAf/sAPPEquWHv09Zp9djJpzY5AZl\nvcypV/4okLjFDPDA6tiN3mJ54lGJ9frBDcCfYlZffwk4sgaPe8IM8Gbgcq26U2cWfp5eb2N4H67T\nvwhfGIBi0HaLLeWcTKC81+tndt/cWywLZNNDC4WjOnA8eTnHAwMZBB0dThCRjwH4cwAvF5FtEXnr\npAwhtYxzhRC/H0Y2i14+zw3b7AwGh9mbtDVrv8CppLOUsaSgXWsAtkOZQYQBXykGfBEgeo7M4A8y\n4FdyDPjpDXbf2sTS/M9+UGJpZnKNn8ezQMPPqpzOCTX9+K2+rNNuAtEs6+KbZaC+xdmx4Uco37T2\nqOmbAVodeKMXyzPtQ54r4OBnZ5hA20WmPajz+xxvFBJvgBeX433eCY1LOMkL69EhtfJFIZpiCWa9\nxiRhgVFKvfmcx58B8Mw459bB/ophRCJAOo1epYKexwNjFgNbRg34y1bQrlpBO+ZQG7a98Cs7LMlU\nimWaw/AFGOR37YC/zsdswglKN3s7QOU+kFpnFhpOUbM+2OHGbWyVow59YaBeBPbuAeEMEL8DNEvA\ncYUdrqEMg0drn8dRkcPFzSC1fsPPACrCDL1d58XBDLDa59Lv3cBQK4bzMAxKP6PWzAfjvOh1O7wQ\njkMgwqqg5t5iBXvbQqFluZROsGKoDYUCpjxedEJoGecKYqRSMJJJ9Go19GozMqOyJZ1alQNQnLK8\nykHkuyXq+E6xA34ocrmk4/MDy5sMfJXth8fTBcIszQTYbWs/7w8DqdsMcvs5Dub2R61qHT9QL1Du\nCa0AkXUACqhvA40C5ZvoHSB8i947vQ4vCM0cpZ7DF4HGNjdzfXEgvOEsyIwigRgevv8o+K19Brfz\nbc8jGOeFp7NgnvLhGC9oM7BQWFR0Zn9FMZaXodpt9MpliN8PCc3g9jSzwsqXWpUSzcrq5V8jAmSy\nrL+vFPl5xGHWZwf83QIDvlLnV+l4vAz4lRwrdZKrbKyx8fqB9C3W4ddyrMGPJJn1J2/RXqGxa5mo\nZantN6ucDFVrcjM3dgc4rnHYSPsletQElriJG1i2jNRabJxSikHYDFws8ZxFdS/fxD2L6TnfL//S\nr7X9fCbUeBSMcmO8UeOUsUUhEObfwGGVgX9CeCFYxZSr4yaEzuyvMMbqKi0Vdnag3JiRjcNSuj/A\nvORwLq0IG62CIdbg111kV7aGH46xSueiOnzTw4DvC3Li1WHt4eeXNik3HJY5+cq+rY+tctxh+wio\n3mPQD6WszVsPm7DqRQb4+B2aqp3sAfsvAkeVfnOSJ0gN3xezDNBc/hfrHJ82V3OC6bPcNkcI2CKW\nhfOE/n5MD3++cxj+fSmhGO84Fs2Hf0bozP4KI4bR77DN52FsbkJmUeOcWrI2UK1h2o4z/DVaKpR3\nrCHaDp0ARViWCbAOX/XYiDUMw6BuXy2w3K7b4VDzweeTa3RrrO/yP7+9cRuMswrnYAfY2wLCSzwS\nt7hx26xS7ohk2H0bSHIM4nGVDVmBBOBPuM/Mbbpt3hn44+6+zrQuDt3WgAWzC3yhyck4gBXsD7lR\nG4xe/vpZEYzwb+KoDkRd9iWcQxsKeVyNi4fO7K844vXCXFvj4PKiw0x7Egxm+MWCM63ZMGic5g8w\n4Ddceo6nV1mZc1C72EtHBFjKUqap1yjrnF1fdIlBvtPixq3t3Oj1U9YJxCjr1LaZMYeWBrL8HVou\nwAAiWSB6m1YHx1Vm+o3iaLLKkTX71ucyQNpum6NKMb6wNRh9QhuNgQg3vxfNIM3j5e+3Xlskv5yZ\nMddgLyI/LiJfFZGeiNw989y7LDvPb4jI6+a1xquAhELU8A8PZ2OpYJNaYtA/PKBbptOAv7rBTdXy\njrvxhgCQylhNW3tA5ZKLTGKZc0mP6lbz1Zn/4IGIZZMs3Li1G4IMo1+d0zmmrHNSZwBL3ALCy8yE\n917ixCjTx6Afu8OsvHUAHN4HDu5R43cikRztskQzkHJ/Z2B721xsjXI+Pqvr14m9shNE2LncbS+e\nVUFyhXd7EzJu80KwBq+jY97MO7N/FsCPAvj84IMi8gqwS+zbQbvP37JsPjXnYCSTkFgMvUpl+pbI\ngyRTp90y3Qb8Ut59wE+keTQOWJ550XtGk30/nfL903Ntgf7GrT/Eblu74xZg7fmDrts8cGA9F0wC\nyTvU5xtlYO8+tX7Tx9LMxN8BghkAAhyVgYMXgf2XmPGf7DOodo7573HNuijsUucPLrn7WQD9ID9q\nSaHp4QVmXMvjQQIRXoTqC+Yp7wuwPHRGYwsXiblq9kqp5wD6t5/hCQBPKaVOALwoIs+DNp9/PtsV\nXi2MTAa9oyN0d3Zg3rkzG/0eoFsm0DdOW1m9PPDYkk4xx4CfWaN7plPiKZ6jWuKIw+W18z1ZgpF+\npU75PpBaO91cY5isvz+o0N+ldUyJx+NlYEhuUtJpVulDE8+yISm+zoy/UQL2twB/DAhZjVyBBI+u\nVWvfbtJOuTWk/NT0A6FVfv0o2PLNOPmQ6R29fHMYIvS7Pygxu/cvUDNTOD4x+4QWgG11NSSheWf2\n5zHM0nN92AtF5G22P3S5fKX9L8ZGDANGNgt0OugVXDpPjksiyQy/fggULhg3OIg93tDO8N1q+NEE\nSzNPjoDiEJlmEF8AyFia+25u+FCLWJpBvtsGdu/3K0pEgEia5ZhQNFOrV/g9+iNA4g6Ho5wcUtpp\nVvvfv+nlRm50HUi+DIg9AkQ2Wbcf2QDijwKx26MHeqDvu2O6rOIZxPCc9hiaBKE4L6SLNpg8vGBm\nbTNi6t+xiHxWRJ4dcjwxifMrpZ5USt1VSt1dnkU36YIjgQD1+3p9+kPLz5JI0kCt0XAe8M9q+G7K\nMgH66Cxbw1MK9y8uq/N4GfD9IaBWBPaGJAeBCJC+zeBXyzHbt78PXxBI3eHmbbPKoG/Pvw2nKe14\nQxyBWHuRg73PYnoBb5Abut7Q6JU7g3Rb1ujDMc5lZ/aT9LURoYXCSWOxtHvDANKbl7/OAT4AG2I6\nOubN1GUcpdRrRviyiVh63lSMZBKq1eJmrd8PIzrD8re45TJYKjLgrzoYZGIHfLssE8p54xUABMPA\nyiZQzgGFLSCzzovHee+1tMbb+HqN1Tip7OlMz+Oljn9QoqzTPgISWUvbtjZv/RFq+LX7LM8MWQ1a\nsTVrGlaZHbhHNVby+B2WmY5C55glo+NgmAAU9f9JBqZwkrr98eFiSTnn/X1cYxb1XuZpAG8SEb+I\nPALgMQBfnPOarhRGJgMJBtErFKBaM25dj8XZbdtoOB9ibmv4duOVG2sFgOWcK1Z+UNy62OFQBEhk\nWJlx0gRKQzZuRdgBGl+lhl+5d7pRyB8Blu7w30aln+UDzNwTt2imBsWGrL37k61lt+n1aOY2brC3\n5+ZO2rHS9qU5biyWG+aEaAHY6vYcHfNm3qWXPyIi2wC+D8AficinAMCy7/w4OEz3TwD8vFJXZBdk\nQbCHlsMwaIk867riWLw/xDzvcBj5g4AfprXCvkut1+sDVm8xMy/lOMz8IsJxyya5y43bYV2foRiw\nfI6sY5jcrI2vUe+u3edGrv287bMTWeV7HOSA2j1q+5MKfMd7ABQloUUlGLPq+CdY7aNxzbyrcT4B\n4BPnPPdeAO+d7YquF+LxwFxbQ3d7G71iEebadOZwnks0BkCA4g4z/Oz65RtjtrVCaYdumb0ekHRR\njujxMMMv5ViW2Wmf76cD0CY5c8uafJVjXf7Z7kqP77Ss02pS1rEnH/kjwFKQpmmNXUoW0RVmtCLU\n+P1RBvmjKpuyDLtiJz76DNhuh949voh7e+OzGAONWeO6X57FZ3X1to76H18TfAA2zfnkzCLybQB+\nAUAawJ8qpX77otcvqoyjmRASDMJIp+ezYQsA0Sh1++NjIHdJxYyNbZ4WidELx+3EK8MAVjb6Ill9\nmwAAIABJREFUU69sm+TzsE3UglFq+btDavdtWSeRtbpu751uzDFMavnxdX7t3hY1/QdlkVbQT94B\nYuusnGlWgOoLtGdoNZzd/dh02xy2IsJyz3EZtFyYNKaHpaoTamS6zojIh0SkJCLPnnn8cavB9HkR\neSfA0nWl1NsB/BMA33/ZubU3zg1grhu2ABCJALLGDdvt+8DaBuC9pKNQhPbIhkE5p9fjQBSnjUO2\nY6bpob1Cp83Pz7uzMAxaLBz6gf0KUGpxI/fs3NJglIFrv0CP/JMGR/LZ5/WHAe9tZvhHNdbhR5ZP\nDwfxhXl0WpRhTg5Zgw9QjvGGqPubvoez/m6HjVhHe9ZglzXeeYyLTEmztwnF2bB23KAD5TWhpYCt\nyVrjfBjA+wF8xH7Aaij9TQCvBcvQvyQiTyulviYibwTwcwD+22Un1sH+hmBkMui1WugVChCfb/pD\ny88SDgPrm5Rz7IDvZA1LGQa8vV3eFWSy7jpFk8sM2NUSUNoG0muUes4jmmJXbXWHG7ep1YeDk8cL\npDZYZVK3bJET2f7QFMOghXIgRuvkwwLLMKOZ04HZ47NM1ZZZwdNuMsNvDtyBicHqGBHeJdhbV4MN\nXJPA7sKdVv25PRP46OBaBXuXpEXkywOfP6mUenLwBUqpz4vInTNf90oAzyulXgAAEXkKbDz9mlLq\naQBPi8gfAfjdi95cB/sbgog8cMjs5nIwNzchl2XXkyYQYMDPb1PSya4DQQcabnLJ6pYtszwzc0G3\n7DCiCQboyg5r8S8qzQQYjDK3+zp+bInHICI0U/OHaJW8e58do5FU/2Lk9QOpW8DRPpuwqveAYIKl\nmoPrF6Hm7guxXr/bYTllr82NTbvZyWsw2/dF+j70k8KWmyZR9z8MEWvIyvWqs/AJsOn8V1FRSt29\n/GUPMazJ9FUi8mrQbsYPByMLdbC/QdhDy7vb2+jlcjA2NiAXZbnTwOfrZ/g7Oer5TgavxJPsuC0X\ngJ0tVu24Wbtdi1/KsTQzneVj52E3YNWKwMGuZaGwyjWc+n6CbMI6KDHLP2lw2PlgBh+McxO3XrGk\nnUMG9fPmvpoewJxiXf5QZlAW6Qv2zeY0Y6OU+hyAzzl9vd6gvWGI309L5HYbve1tKDebgpPC66WM\n4/Ew4Dcc1p9HYgzy7RYDvtv+AZ//dGnm4d7FrxehjJPIsB6/PGCFPIhhMMAnstw4Ld8DGmfObZhA\nbIXGaoaH0k71Hi8Oi4D9d+B22IobfEEA6lpt1LYUsHXs7BiDiTSZ6mB/A5FgEOb6Ojdt83moeTS7\neDzM8P1+btweDrEWGEYoDGQ3qTEXtoATl/+L7NLMYJg6/mWVOgAQSbBaRylaJQ/z1QG4eZu+TWnn\noATsbj/sN+MNUNqJZRlg93P0zJ93DXqrzr2BSctDgwSjAISlqxo3fAnAYyLyiIj4QEfgp92eRAf7\nG4qEQjBWV6GazdkOPRnENJnhB4O0R96/JNO28QeA7C1moYVt4Mhl8DAM6vaxJLP7cv7yskdfgDq+\n7atTLQz/GtNDB834CgN4+SWgOeRCFoiyAzeyzA7Y2n1gf2d+g7pbTUpNo9okOyUQAY6vj72wT4DN\ngLPDCSLyMdDd9+Uisi0ib1VKdQC8A8CnADwH4ONW46krtGZ/gzFiMaDdRm93F71AAEYiMYdFGNyo\nLewAZWv6VNzBOrxeZvjFHI/0ijs/HYCVOl4fs3t74/ZsqeUgpsmRhwe7fR0/lR2+2RuKc8N1v8Dj\n+JAXgMHqGRF66gTi1PKblp4fiPPxSZRUOqFZ452SfwZVMl4/fxa93o10nrwMpdSbz3n8GTjYhL0I\nHexvOMbSEtTxMXqlEmCas6/BBxj0VrP9gN/tcgrWZXg8DPilPDdu22133bYAEImfrtRJZ0973Q8j\nttQfal6+T00/PGRurMfLSViNPeCgzCw/tswLwSCGwQqdYMKqz98HjveZaQeT0+06PT5k568/Ml2z\ntoe4Hj45rR6wdUVUKX1p1cBYW+ubps1yytUgdsCPxoDqbj/LvwzbT8futi07nJY1SCDEjVvDBIrb\nl2/c2l+zcpuB+CJZBwDCCWD5jtWMVeQIxGHzXg2TtfjpRxn8W0fsxK3e4wVg0pvpRwfs3vUGuYcw\nCzzWXdBFVtSaqaAze82DGvze1hZr8G/dgvhmJCGcXgjN00wT2LO6ZjMrl+vIdretx8uA32mzFv9s\nmeRFeLwM+JUdyjqtE867vei9TQ+wvOFM1vF4gaUN6veHZdothJOszT8rZximZZucYjPW0Z7VnFWi\n1BKIWV74I+ZqSvXLQL0hGrlNW6vvvzn/mdn7TRefAWwusAfdIDrYawCwBt9YX0d3awvd7e35NF3Z\npJcZqHcrrGZZddhElVyill8psjQzs8a6fqfYG7d7FWC/yhLPZQcXjZjVXGXLOrH0w2ZqNqEYNykP\ny5zgdHQIxDN87CwirNEPxpnlnxxSdjmpAxBm5L4QrRdM7+U/I6X4tfUKG7aCCW4QzzLw2qWd16y5\n6iqgg73mAeL1sulqawu9fB7G5ubs5tieJZmiJl8q9rttnTRRRWJWHX3eGmSyBgRcat6JNDcSdwvA\nzj0GfP8l5RT+IKt19oo0UztusEZ/mJ2BYXCzNhgD9ktALc9gH02fvynrC/KIWNYKJw3aKzQqPIB+\n6aTpBaAGhpAoaxbuMT82fUBiY3ynzFGwg72av7/7JGj1gK0rUlykNXvNKR40XZ2coLezM58afJto\njFl9q8WA77SJKhBkaaZhsjSz7rCGf5BwlLIOwI7buoPOT9OkeVpyhZl48R7QvKCByBekdXI0bTVt\n3eNG7kXavG2tEF0GUreBpUept0eWrdJJg0G906LPTqvBtQC8Q0hssORzHoHeXj9wLQeZLDo6s9c8\nhIRCMFZW0CsWoSoVyDxn+w4aqOW2GPyd+OnYpZnlHW7atlpAyqUVsM8PZG9Tx98tsoHrMh0fYGWO\nLwjUCpR2jg5ZsTMsyxehn04oTqMwW9qJpin5XIbpAcw5VFCNiu3ieU0ye58BbM7a2WJEdGavGYoR\nj8NIJNCr1dDbn7OfSSAAbNyi/JHfdm6vYJqs1InGqcGXhvjUOzlHZp0DUOr7lw81t/H66K0TX6ak\nU7wHHF1wv2+YlHaWbjGA7xeY6bfmVB01LdQMbBk0Q9GZveZcZHkZ0m6jVyxCPB5IeI7WtF4vA35+\nm1n+csZZ85UIG648XqBW4aZrZu1yP/2z50ikqcvbOv5lRmo20SRdNGsFumgGo1aWf86mry9Aaefo\nkJLO7hYll0hqsQZ2j8osPHhmSKsLbI2gEs6Dec+g/TUR+bqIfEVEPiEiiYHn3mVNZfmGiLxunuu8\nqdhzbMXvRzefhzqes3+LaVLSCYdZh1924Gtjk0gxy++0gZ377i0WAAb3QSO1WtnZ+3t99NaJLdEq\noPjSxVo+wIvC8h1q850WUN2m184wI7arhO0BdFGnsmYqzPvy+hkA36GU+k4AfwvgXQAgIq8AzX6+\nHcDjAH7LmtaimTFiGA+skLu5HJRbp8lJY9srJJL00inknQf8UBhYu02ZpLDtfqA50K/HjyY4Aau4\nDXQ6l3+dCIN95rY1TGWH4w8vGtNoGEAkCWQesYL+CX3zazsPG6xdFTotZvWzsoKYMj4T2Iw5O+bN\nXIO9UurTlskPAHwBtO4EOIXlKaXUiVLqRQDPg9NaNHPArsEHgO72NpSTObLTJr1MKafRoLTjtLvU\n3rgNRTgMpVJ0r+OLcKM2nWUn7M5LQMOhbe8DLT/tPMsXYdBffgQIp/h1pRfZjTsv47RxuCYNVVeN\nRdLsfwbA71kfr4PB32bbeuwhRORtAN4GALdu3Zrm+m404vPB3NhA9/79/uCTeRtZxRPc2CwV+pU6\nTrR4wwBW1oCq1TzVOqGO73aQSzjK+vvKDo+TI5qrOQlm0RS7Ye2KneYBSzYvGjNoGGzYCsWBRpVf\n09zn55GlyY0onCa9zrXR6wFLs3do1jpvpv5TF5HPisizQ44nBl7zbgAdAB91e36l1JNKqbtKqbvL\n8ywRvAGcqsHP5eZbg28TjVLWabc529aNt08qzSDfbgH5++698QFKMiubfbvkwn1ePJzg8/crdk6a\nl9flD75nfAVYeZSWC80DZvoHlcn750yak+b12Gi+gkw9FVBKveai50XkLQDeAOAHVD96TGQyi2by\nSDgMY3UVvZ0ddtmurUHmfVseCrFSp5CnpJNZYUOWE8IRwLsJFPO0WBjFKlmEGX0gxGqdwn0g5fA8\nIqcrdi6ryx/EMC0XzQRHIjaqNEwLJWi+ZizYNtdJk6WX82romgI+E9icgzM4AIjIDwP4IQAxAB9U\nSn36otfPuxrncQC/COCNSqnB8oinAbxJRPwi8giAxwB8cR5r1DyMEY3CWFmBajTQKxTmvRxiz7a1\nB6FUd118rR9Yu0VJplwAdl1U+QwSDAOrt/slmpULnDDPYlfsDNbl1x3qAx4vxyKmLRfO+i4z/cPd\nxcr0jw4o4QzzAdIAAETkQyJSEpFnzzz+uFWZ+LyIvBMAlFL/Uyn1swDeDuCfXnbueYt87wcno3/G\nyg6/oJR6u1LqqyLycQBfA+Wdn1dKLcCuoMbGiMeBbhe9SgU9jwfGIkhopklJp1RksG+3nblm2l+7\nusFa/P0apZjlrHsd3+MBVjbovrm/Sx0/nb3cWwc4neXvlXg09qnl+xx8vdcPJNe4aVzf5dHco9QT\nSsx3WEinzWAfTl6rDdpWB9iqTvSUHwbj4kfsB6xKxN8E8Fpw//JLIvK0Uupr1kv+rfX8hcw12Cul\nXnbBc+8F8N4ZLkfjEiOVYsCv1QARGGmXdgTTwLZJ9vnomtlpc+PWid2xCJBaZmCtFID8PQb84Aiy\nQ2KJsk5lh946iTR1fSd4fbRObh7SVK10n3NwY2lnAdsO+q1jBnzbhmGeQb9hRcSww5/B9SQtIl8e\n+PxJpdSTgy9QSn1eRO6c+bpXAnheKfUCAIjIUwCeEJHnAPwKgD9WSv3lZW8+78xec8Uxlpehej30\nqlXA45nPaMNhJFNW81OBG7fZded2x5EoX1vaYT1+Ms2mLLcEgvTWqRbZgHXcpJbv9G4hFGWWf1Ch\npHPcoMwTdCiD+AKch9s6ZsA/rAD1Khu2QgleFGbBSZNVQ+Hk1agYcoHPA2w6/9OoKKXujvA26wC2\nBj7fBvAqAP8cwGsAxEXkZUqpD1x0kuv1k9fMBXNlBd1Oh6MNlYKRXJDsLRplKaZbEzWgr+NXipR2\nTo45IMVtVmyatEg+3GPAL9xjwA85DNiGwc3aYJQTsXbzDPZONnAffC8BDk5pHVPWsUs2/WEG/cAU\nbTCUAg5KbKKKLsCd3zVCKfU+AO9z+vrrU/CqmSvG2hokGkWvXKassygEAty4NU1W6hy6MDIxDCCT\nBZYyQLPO8sxRO4ijCWb5pgco5+mi6WYT2B/kGMR4mhl+4UXgsOruHL4AN3JXHmVdfvsYqOWA4gu8\ne5i0FYMd6DstBvprpNXPmIlUJ+rMXjMRRIQlmUqhV6ZnjJEaQfqYBnalTnGHlTrttrOB5jaxBDP9\nUp6+OstZWi+4xeuj1cJehVYLJ0fA0qqzzVvA2sBNMcvfLwP7Fer6ydXhoxDPwzCB6BLN1Y7r3Dht\nVHkYHmb6/gjr4UcN0J02p3Ed1ynfXNMKnFYH2KpM/W2+BOAxqzIxB1rJ/ITbk+hgr5kYtnFar1BA\nr1Lhpu2iSDpnK3VaLW7kOg1m9kCUUh4o5rgBm3RxwbCxa/KDYW4CF+4D8SVaKDtdi8fLISlHdVbs\nlO/zIhB1cQ57LcEoj16XdwwnDdb5N/cBCN/LZ40/9AYuNzBrHTHAN/YAKHr6RBbkb+AKICIfA/Bq\ncDN3G8B7lFIfFJF3APgUABPAh5RSX3V7bh3sNRNFRGBms+gCzPCBxQn451XqON0w9Xr7Ov6eVVa5\nnHU32NwmEALW7nC4+f4uA+3SKrN/pwQjDMT7ZQ48bx6428AdxDA5LCUUs2bVNoFWkxLMg+APZv6+\nAF8vJgDF16ge/7VnywZjlIquubulzwNsTnArQin15nMefwbAM+OcWwd7zVQ4Jen0ejCWRsiCp0Uy\nxaBa3OlX6vgdyiAi3KgNBNl8lbtHXd/tnFuAewLpVWb5tRKz/GTGXQevaXLWbSjGLH83T/klkXF3\n4RhEhFKOvXGrrIDePuZFoHPCoG43bHl8/F784f4xb98kzUPoYK+ZCiICc20N3UIBvV12ci5E45VN\nJAJ4b7FSZ/s+sJLlY06Jxqm12zYLqWUgPuIdTDjKDdjKDjtvjxsM+m7uGAIhbuDW99g5W7o3mrQz\nDBGWaXr9NF3TPKDVBraK816FM/TlVzNVzNXV/njDyvR3slzh9wObt/lvIQ/suawi8vmB9dt9u+Ri\n/mJ/+ovweIDVTTZfNeuchnXkcPyijd2Bu/IIdfiDXdouHI8wqEVz7dCZvWbqGJkMlFJsvPL5YMQW\nYJKDjT39qlgAKmVu3C47GCpuY9sl79dYjz+OrANwozYYZoZfygGRODd03cgip6SdIlDZtsYhLl+7\npqZ54/MCmyvzXoUzdGavmQlGJgMJhVipc+hw0MesEAFWs9TyD/Yp7bjN0ONJbt4aBmWdvTEMU3x+\nlmjaQ87zLzHbd0sgBKzcOT0O0W1tvubaoC/zmpkgIjDW19Hb3kavUOAAc6fdrLNiKW3ZJBTdWywA\nD3fdHh9xM3eUah17yHkwQruFcp7avlst3x6HaG/g7lvWC7E0EF6gO6wrSqsNbO3MexXO0Jm9ZmY8\nGGDu9XK84bwHmA8jGqOs0+u5H4YCnO66PW5S1hllKIqNP8AsfxwtH2AJZHodSG9QyqkVaLDmdNCK\n5sqjM3vNTBGPB8bGBnpbW+jm8zBv3YK4tRGeNoEAh6Hs5NwPQ7GJJajbD1brxEY0iRPpa/mVHWr5\n0QQvAG5LHAMhIHCLnbd7JVbthOPM9Ee5A7nh+LzAZnbeq3CGzuw1M0c8Hhhra0Cvh97WFlS7Pe8l\nPYzXe3oYyu4IlUS2rBMIsSa/XBhPL/f56a8TTdBYbecepaJRCEWB1UfY3drYB4ovUt7Rev61ZcFS\nKs1NQfx+DjDP5dDd2oK5uQlxMix8ltgWC+USUKvSU8eNxYJ9jtV1btjWKv2hKG72AgYRAVIZBuvd\nAr3y4ylaLritpzcMVuiE4/1hKfU9PjZNJ8xrRKsNbF2Rgak6s9fMDQkEYG5sAEqhu6gZvghlnKU0\nUD+kVfIotfSJFCdhdTs0U6uPWZFke+VH4sB+lVn+qHsD9rCUpTV+XskB5W2t518zdGavmSsPMvyt\nLfS2t2FsbCxehg88bLGwuubcYsEmGALWbgPlHR7HTWr5o1oLGAawZHnj7xZHM1U7tb4IM/rGPhuy\nHuj5S7o+/xx8XmBzfd6rcIbO7DVzxw74qttlhn+yoBllJEIdXykG/FH6BTweZvjxFHC4P361DsCN\n27U7LKXc36W00x7Rd1+EIxBX7lDPbx7QO79WHL07WLMQzPVyLSK/DOAJAD0AJQBvUUrlhdPHfwPA\n6wE0rccvnbGoubpIIADz1i10t7ep4a+vL14dPtCv1Cnu8Gi33HnjA5bunqYnfrnAap1kenRvHaBv\nqhay6vJ37rHzNjpiBZBpUruPJIB6jVp+82ByfjvXhFYL2Nq6/HWLwLwz+19TSn2nUuq7AHwSwC9Z\nj/8ggMes420AfntO69PMEPH5uFHr8bAOv7mgni4eD7C2wXLM6i5LNG0HSDcEgvTWCYbprVMYoXP3\nLKEIsHqbFUDVElAYI8sHWJ+fyDDTD0Qo7+y8oCt3FgAReVREPigiv+/k9XMN9kqpwRlxYQD2X88T\nAD6iyBcAJETkilSzasZBvF4Ym5sQnw/dXG5xA77tjZ9ZAZpNyjqjjCy0vXUeNGG9BByN+T17PEBm\nnXp++4RZfn1/vHN6fcBSFljepPvlXon2C0cj2DhcI3w+YHPT2eEEEfmQiJRE5Nkzjz8uIt8QkedF\n5J0AoJR6QSn1VqdrnXdmDxF5r4hsAfhJ9DP7YdPUh26DiMjbROTLIvLlsjUsQ3O1EdPkRu2iB3wA\niMWZ5Xe7DPj1EYNfLGF565hAYRuoVsbPnCNxIHuH9sm7RTZjdcasePIHWbmTXgfEoH9+eYuDXDST\n4MMAHh98QERMAL8JKh6vAPBmEXmF2xNPXbMXkc8CWB3y1LuVUn+olHo3gHeLyLsAvAPAe9ycXyn1\nJIAnAeDu3bv6vvKaYAf83vY2urkczLU1SHhBa7+DQVolF/I8ltKs3nGL3YRVLbOc0p6ENU6HsccD\nrGywCWuvwiw/kR5dy7cJhDkkpXlAaae8xQlWsaUbVaM/ac1eKfV5Eblz5uFXAnheKfUCAIjIU6D6\n8TU35556Zq+Ueo1S6juGHH945qUfBfBj1scTmaauudqcyvDzeajGCJ4ws8LjYaVONMZu28LOaDq+\nYQDpFRqotU4o64xbkw8wuGdvMyBPQssHKGWF4+zETWQ4vaqSAwovAY0Drek/TNpWIazjbQ6/bqjS\nISJLIvIBAN9tJcsXMu9qnMeUUt+0Pn0CwNetj58G8A7rCvYqAPtKqSviLaeZJA8y/FyOXjrr65BQ\naN7LGs7ZGbftFjtwR8nMIzFKJnZN/lGDuv444/48Xmb59X2gVh6/YsfGLtcMx+m5U6/RaG2/bD2e\nuLa+O7Zm75CKUurupN5bKbUL4O1OXz/vTolfEZGXg6WX99Bf+DNg2eXzYOnlT89neZpFQEzzgT1y\nN5db7IAPUMLx+fsNWCtZSj1u8XqB7CatFvZ26YOTydIJcxwicUot1SKz/MYhyzbHHQ4uwlr/cIyj\nFet7lHjqe5ygFU7o2bSjMRGlY67BXin1Y+c8rgD8/IyXo1lgHmT4tlvmotbh24TDlHUKeTpnppeB\n+AgZtAiQXGL3bWlnfAdNG7tip37AYed2lh+Z0IxZe2B56wQ4qNBH/7DKGbbR5LXpyKVmP/Vmsy8B\neExEHgGD/JsA/ITbk+jLrObK8EDDX/Q6fBt7xm0oRDO1UnF0HduuybcdNIv50fYEzhKJsWLHF+hb\nLoyr5Q/i87NyJ3Mb8Icp8RReBKoFoLWA8wzmjIh8DMCfA3i5iGyLyFuVUh2weOVTAJ4D8HGl1Ffd\nnvt6XF41NwbxeGBsbl6NKh2AskV2nRp+rcpAuro2moZtO2ja8263X2KN/riyjl2xUz8A9iwtP74E\nxJKT65T1+Vmn311mht884OELsCM3EL6SXbnU7Ce3H6GUevM5jz8DytsjozN7zZXjbJVOb9Ta9lmy\nlKZ2f3xM58xxHD7jSWr5IkD+PlDbnUzli53lhyL9Ms1xfXvOYnpYubP6aL+CZzcP7HyL2f7xgt+t\nXWF0Zq+5kohpMsPP5dDL54GVFRjxCenN0yIaZRZdyPc3bkfdaPYHKOvslrh5e9RgTf64jqGmCaSz\n3GS1ZZ1Ykpn+JDdXDYOVOpEEu3CP6tzUbR6wQzcU47HgVTytlsLW1gRlrymiM3vNlUUMA8b6OiQc\nRq9YRK9anfeSLicYpJGaadJT52AMGwPDYD3+cpbyUO4lOmlOZJ2Wk2YkDhzUgPxLo82+dfReESC1\nCmQfBZIrvGPZLwOFF+i2qbX9iaAze82VRgwDxtoaeoUCepUK0OvBSKfnvayL8Xr7zpmlIqWd5czo\nmnUkyg3cSgGoFOmtk14ZPxO3/fLDMZZplnIM/skxPPgvwm7SCsdZxVOvMdNv7AMeH3X9YIT9BwuC\nzyfY3Bxx6tiM0cFec+UREZjZLLqGwexeKRjLy/Ne1sWc3bjttCnrjCpb2D759vjDk+PJ1OQD/alY\nexVm+UcNazRiZPxzn4fPz2y/lwGODinzNPZ4AfB4gWCUwd8XuJIbu/NAB3vNtcFcWUFPBL1ajQE/\nk5n3ki5nKc2SjlJx9AlYgyRSDM52TX5iiY+Niwgz+lCUWX45T6kntTKed89lGEY/21eKHbrNAwb9\nwyrX5fUz8/f6mfWb3plp/dTsr4bMpIO95lphZGgp0KsyECx8hg/QT8fr62/cZla5mTsqdk1+pcgs\n//iI2v4kAqA/AKze6hur5V+ksVpsjMErThns0O12gdYRzeLax8BJkxeBQQyT1T/+IC8EppcXj16P\n1s83DB3sNdcOI50GlGKGL7L4Gj7w8ASs4yN23Y4qUZgma/AP9uiimbtHWScwAb1bhME9FKHdQq3M\njDuZmYxs5ATTpH4fHJCSuh0G/24HUD2WdbZbvAicbUCbkPRDzX5G3/OY6GCvuZYYy8tQvR4z/F7v\nakg69gSs3QqwV+Mm5agNWDaxxGlZJ5mejKwDUDu3LRf2yizTjCaY6c/DA8f0UGYaRqfdvwhAeCd1\nw9DBXnNtMVdW0DNN9KpVqF4PxsoKZNE380SY0fv9k9PxbZ/8B7JOk+Wak9K1I7F+I9bhHtCsT38D\n1y0e7/hGb0NotXrY2roajWC6zl5zrTHSaRjpNNTBAXr5PNRV8ViPxmikphQ7bsf18jcMyjjpFUpE\nuZeAxgQ7jw2DAX7V6iEo5yczGUszMXRmr7n2GKkUYJpsvMrlYKytQa6C1a6t4xfybMBKLwOJMTdC\no/G+T34pD4Sj9MmfVJY/uIG7v9v32YkmrmWJpM9nYHNzge22B7gCf/EazfgY8TiM1VWoZhO9rS2o\nSThGzgJ7AlYkClTKQLEwvtulz0dZJ5mm5JK7N/6Q80HsDdzsbV5Y7EEpx3pO7TzRmb3mxmDEYhDT\npHna9jbN1K5Chi8CrGaBmp+bt62T0SdgDZ4zkQJCYW7eFraBeIre+ZPKwO0N3KMGq3aKW/PdwJ0C\n1OwnMDZyBlyPn7hG4xAJh2Fks1AnJ8zwu1MfPDE5kikG+XYb2LoHHE0gU7Y3b6NxDjnfGdORcxjB\nMLP8WJLyTv5FjkbUzBSd2WtuHEYkAllbY4a/tfVgIMqVIBymjr+T4wSszAo3c8fBHnJKKSSQAAAH\nJUlEQVQeCNHpMn+P07CiE3QRNYx+B26tzPep78+2Nn8KULMfowFuDEQkDOC3ALQAfE4p9dGLXq8z\ne82NRMJhmBsbUJ0Oernc1crwfT4G/GCQGv44E7AGiUSB9TsMvpUip2FN+ufiDwCrm8DSKit1CvcZ\n+K/Sz3+KiMiHRKQkIs+eefxxEfmGiDwvIu+0Hv5RAL+vlPpZAG+87NxXJJ3RaCaPBIM0UMvnoQ4O\nIMkZtPxPCtPsN2DVqszuJzGT1zZUs6dhNQ7Hn3c7DLs2f3+X5mqmhz4+V4xWq4utrYlKUh8G8H4A\nH7EfEBETwG8CeC2AbQBfEpGnwcHj/8962aVXS7kydccOEJEygHtzXkYaQGXOawAWYx2LsAZgMdax\nCGsAFmMdi7AGAHi5UmosDUZE/gT8fpwQADDomvakUurJIee8A+CTSqnvsD7/PgD/Tin1Ouvzd1kv\n3QZQU0p9UkSeUkq96aI3v1aZvVJq7q5XIvJlpdRdvY7FWMOirGMR1rAo61iENdjrGPccSqnHJ7GW\nS1gHsDXw+TaAVwF4H4D3i8gPAfhfl53kWgV7jUajuSkopRoAftrp6/UGrUaj0Sw2OQCbA59vWI+5\nQgf7yfOQBjcnFmEdi7AGYDHWsQhrABZjHYuwBmBx1nEZXwLwmIg8IiI+AG8C8LTbk1yrDVqNRqO5\nyojIxwC8Gtz0LQJ4j1LqgyLyegC/DsAE8CGl1Htdn1sHe41Go7n+aBlHo9FobgA62E8IEfllEfmK\niPy1iHxaRNasx0VE3md1vn1FRL5nimv4NRH5uvU+nxCRxMBz77LW8A0Red201mC914+LyFdFpCci\nd888N8t1DOs6nDrDuiBFJCUinxGRb1r/TrWDS0Q2ReTPRORr1u/iF+a0joCIfFFE/sZax7+3Hn9E\nRP7C+t38nqVFTxURMUXkr0Tkk/Naw1xRSuljAgeA2MDH/wLAB6yPXw/gjwEIgO8F8BdTXMM/AuCx\nPv5VAL9qffwKAH8DwA/gEQDfAmBOcR3fBuDlAD4H4O7A4zNbB6htfgvAowB81vu+YkZ/C/8AwPcA\neHbgsf8E4J3Wx++0fzdTXEMWwPdYH0cB/K3185/1OgRAxPrYC+AvrP8HHwfwJuvxDwD4uRn8Xv4V\ngN8FG5YwjzXM89CZ/YRQSg2Otg8DsDdDngDwEUW+ACAhItkpreHTSqmO9ekXwBItew1PKaVOlFIv\nAngewCunsQZrHc8ppb4x5KlZruOVAJ5XSr2glGoBeMp6/6mjlPo8gOqZh58A8DvWx78D4IenvIYd\npdRfWh8fAngObM6Z9TqUUsoeieW1DgXgHwL4/VmtQ0Q2APwQgP9qfS6zXsO80cF+gojIe0VkC8BP\nAvgl6+Fh3W/rM1jOz4B3FPNcw1lmuY5F+Z5tVpRSO9bHBQArs3pjq/3+u8GseubrsOSTvwZQAvAZ\n8I5rbyAxmcXv5tcB/CIAe/LL0hzWMFd0sHeBiHxWRJ4dcjwBAEqpdyulNgF8FMA75rEG6zXvBtCx\n1jEVnKxDMxxF3WAmZXAiEgHwPwD8yzN3nzNbh1Kqq5T6LvBO85UA/u6033MQEXkDgJJS6v/O8n0X\nDW2X4AKl1GscvvSjAJ4B8B5MqPvN6RpE5C0A3gDgB6z/zJj0Gpys4xwmvo4FeS8nFEUkq5TasWS8\n0rTfUES8YKD/qFLqD+a1Dhul1J6I/BmA7wPlTI+VWU/7d/P9AN5o1aoHAMQA/MaM1zB3dGY/IUTk\nsYFPnwDwdevjpwH8M6sq53sB7A/cRk96DY+Dt6pvVEoNDhV9GsCbRMQvIo8AeAzAF6exhkuY5Tom\n0nU4QZ4G8FPWxz8F4A+n+WaWJv1BAM8ppf7zHNexbFeFiUgQtOl9DsCfAfjHs1iHUupdSqkNpdQd\n8O/gfyulfnKWa1gI5r1DfF0OMIN6FsBXQAe6detxAb2ovwV6T9+d4hqeB3Xqv7aODww8925rDd8A\n8INT/ln8CKiBnoBdgJ+a0zpeD1ahfAvAu2f4t/AxADsA2tbP4a2gRvynAL4J4LMAUlNew98HJZqv\nDPw9vH4O6/hOAH9lreNZAL9kPf4oeKF/HsB/B+Cf0e/m1ehX48xlDfM6dAetRqPR3AC0jKPRaDQ3\nAB3sNRqN5gagg71Go9HcAHSw12g0mhuADvYajUZzA9DBXqPRaG4AOthrNBrNDUAHe821xfJzf631\n8X8Ukf8y7zVpNPNCe+NorjPvAfAfRCQDuj6+cc7r0Wjmhu6g1VxrROT/AIgAeLWir7tGcyPRMo7m\n2iIifw+c2NTSgV5z09HBXnMtsex7Pwo6kNYtR1CN5saig73m2iEiIQB/AOBfK6WeA/DLoH6v0dxY\ntGav0Wg0NwCd2Ws0Gs0NQAd7jUajuQHoYK/RaDQ3AB3sNRqN5gagg71Go9HcAHSw12g0mhuADvYa\njUZzA/j/x0BKq6VFkM0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ad9596630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "cm=ax.contour(x, y, z, levels=np.logspace(0, logzmax//2, 55), norm=LogNorm(), cmap=plt.cm.jet, alpha=0.15)\n",
    "plt.colorbar(cm)\n",
    "# ax.plot(*minima_, 'r*', markersize=10)\n",
    "# ax.plot(*problem.x0, 'r+', markersize=10)\n",
    "plt.title('minst: a slice of the problem surface for 2 neurons: loss')\n",
    "ax.set_xlabel('$x$')\n",
    "ax.set_ylabel('$y$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:18:13.832754Z",
     "start_time": "2017-11-18T00:18:13.827891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.494302237578435"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scale lossses to they look OK\n",
    "v=(v-v.min())*10000000\n",
    "logvmax = np.log(v.max())\n",
    "logvmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T00:18:15.365875Z",
     "start_time": "2017-11-18T00:18:14.848207Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/matplotlib/contour.py:1518: UserWarning: Log scale: values of z <= 0 have been masked\n",
      "  warnings.warn('Log scale: values of z <= 0 have been masked')\n"
     ]
    },
    {
     "data": {
      "image/png": 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buxFhWEmliOp11fK7J8WcViqFu7CgUlXl8q3LVL0qENtWWbBYWSSo17WhaqtF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\nrQYOQzEHdhyEtZr0T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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0ad8a120b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "cm=ax.contour(x, y, v, levels=np.logspace(0, logvmax//2, 55), norm=LogNorm(), cmap=plt.cm.jet, alpha=0.15)\n",
    "plt.colorbar(cm)\n",
    "# ax.plot(*minima_, 'r*', markersize=10)\n",
    "# ax.plot(*problem.x0, 'r+', markersize=10)\n",
    "plt.title('minst: a slice of the problem surface for 2 neurons - variance')\n",
    "ax.set_xlabel('$x$')\n",
    "ax.set_ylabel('$y$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Generate path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model3 = torch.load(model_path)\n",
    "# freeze all except last layer\n",
    "mlayers = [\n",
    "    model3.conv1,\n",
    "    model3.conv2,\n",
    "    model3.fc1\n",
    "]\n",
    "for layer in mlayers:\n",
    "    for param in layer.parameters():\n",
    "          param.requires_grad = False\n",
    "            \n",
    "optimizer = optim.SGD(model3.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epochs=100\n",
    "points2=[]\n",
    "for epoch in range(1, epochs + 1):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        if cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        \n",
    "        # reduce this to a binary problem\n",
    "        target = (target>5).type(torch.FloatTensor)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        output = model3(data)\n",
    "        loss = F.binary_cross_entropy(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        x,y=model3.conv2.weights.data\n",
    "        z=loss.data\n",
    "        points2.append([x,y,z])\n",
    "    \n",
    "    \n",
    "    print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "        epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "        100. * batch_idx / len(train_loader), loss.data[0]))"
   ]
  }
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